Day-ahead operation method for virtual power plant in distributed coupling market containing residential users

Abstract

The invention discloses a day-ahead operation method for a virtual power plant (VPP) in a distributed coupling market containing residential users. The day-ahead operation method comprises the following steps of: firstly, performing a day-ahead operation on the VPP in the distributed coupling market; based on a distributed coupling market in a power distribution network range, a VPP day-ahead operation strategy is constructed, and the operation income and the green low-carbon property of the VPP are further improved through distributed market transaction (distributed transaction for short). The method comprises the following steps: firstly, constructing a VPP distributed transaction model considering resident users, including distributed electric energy and green electricity transactions, and enabling the VPP to perform electric quantity interaction with transferable and non-adjustable loads of the resident users and form electric quantity prices, thereby realizing reasonable distribution of distributed transaction benefits; then, considering a distributed transaction model, and constructing a VPP day-ahead operation optimization model under the distributed coupling market; finally, the effectiveness of the method is proved through example analysis.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and dispatching technology, specifically relating to a day-ahead operation method for a virtual power plant in a distributed coupled market with residential users. Background Technology

[0002] Large-scale grid connection of renewable energy is the key to promoting my country's energy transformation. However, the randomness, intermittency and volatility of renewable energy will quickly consume the power system's flexible adjustment resources and increase the technical difficulty of maintaining power balance in the power system. As a new type of business entity, virtual power plants (VPPs) can aggregate and dispatch resources such as load-side renewable energy generators (e.g., distributed photovoltaic, wind power), thermal power units (e.g., micro gas turbines), energy storage, and adjustable electrical equipment under the drive of business models; and actively participate in market transactions (e.g., electricity trading). In this way, fully tapping the flexibility of the load side will help improve the power system's ability to maintain power balance. Based on the electricity-carbon-green certificate-green electricity coupling market (hereinafter referred to as the coupling market) that my country is building, the construction of VPP operation strategy is the key to improving the VPP business model. Specifically: (1) The various transactions under the coupling market provide VPPs with rich and stable sources of income, which is the basis for improving the VPP business model. On the one hand, VPPs can participate in centralized coupling market transactions at the national and provincial levels, including centralized electricity trading, carbon trading, green certificate trading and centralized green electricity trading. The centralized market has many participants, providing VPPs with a large number of trading opportunities and rich and stable sources of income. On the other hand, VPPs can participate in distributed coupled market transactions within the distribution network, namely distributed transactions, including distributed electricity transactions and distributed green electricity transactions. Distributed transactions have fewer participants and limited trading opportunities, but higher returns. (2) The VPP operation strategy under the coupled market, including the VPP trading strategy under the coupled market and the VPP internal resource generation and consumption plan under the trading strategy, is the core content of the VPP business model under the coupled market.

[0003] Against this backdrop, existing research on VPP operation strategies still has shortcomings in the application of distributed trading strategies. Specifically, current research typically constructs distributed trading strategies among multiple new operating entities such as VPPs, involving a single type of entity. Further exploration is needed to develop distributed trading strategies between VPPs and residential users in the distribution network. Among these, residential users are the most geographically widespread, and their electricity consumption growth rate in 2024 is the fastest among all user types. Therefore, the extensive connections between VPPs and residential users in the distribution network need to be considered.

[0004] In view of this, this invention constructs a day-ahead operation strategy for VPPs under a distributed coupled market including residential users, and further improves the operating benefits and green and low-carbon characteristics of VPPs through distributed trading. The distributed coupled market described in this invention is in contrast to a centralized coupled market. The former involves direct transactions between VPPs and residential users within the distribution network, while the latter involves transactions between VPPs and power grid companies or trading centers in national or provincial markets. In other words, the distributed coupled market described in this invention refers to a localized electricity market within the distribution network, directly conducted between virtual power plants and residential users, including distributed energy trading and distributed green electricity trading. Summary of the Invention

[0005] The technical problem this invention aims to solve is how to consider the scenarios of distributed transactions involving both Virtual Private Cloud (VPP) entities and residential users, and how to construct a day-ahead operating strategy for VPPs in a distributed coupled market. Furthermore, it provides a day-ahead operating method for VPPs in a distributed coupled market including residential users, supporting VPPs' participation in distributed transactions involving residential users and fully leveraging the role of distributed transactions in improving the economic efficiency and green, low-carbon characteristics of VPPs.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a method for day-ahead operation of a virtual power plant in a distributed coupled market with residential users, comprising the following steps:

[0008] S1. Establish a trading framework for distributed energy trading and distributed green energy trading between virtual power plants and residential users within the distribution network. The residential users include two categories: transferable loads and non-adjustable loads.

[0009] The distributed electricity trading and distributed green electricity trading are carried out simultaneously in the same distributed coupled market;

[0010] The virtual power plant and residential users jointly determine the electricity price based on electricity supply and demand and flexible adjustment capabilities;

[0011] The virtual power plant interacts with both the transferable and non-adjustable loads of residential users, including:

[0012] For non-adjustable loads, the virtual power plant supplies them with electricity and ensures that their power consumption remains constant;

[0013] For transferable loads, the virtual power plant supplies them with electricity and utilizes its flexible adjustment capabilities to adjust their power consumption within the time frame allowed by residential users.

[0014] S2. Based on the transaction framework of step S1, construct a distributed transaction model for virtual power plants, including: modeling of distributed transaction revenue and modeling of distributed transaction constraints; the modeling of distributed transaction revenue includes: distributed electricity transaction revenue, distributed green electricity transaction revenue, and grid access cost; the modeling of distributed transaction constraints includes: electricity interaction constraints between virtual power plants and transferable loads of residential users, electricity interaction constraints between virtual power plants and non-adjustable loads of residential users, and renewable energy consumption constraints for green electricity supplied by virtual power plants to residential users;

[0015] S3. Based on the virtual power plant distributed trading model constructed in step S2, establish a day-ahead operation optimization model for the virtual power plant under a distributed coupled market. The day-ahead operation optimization model aims to minimize the total operating cost of the virtual power plant, which includes equipment operation and maintenance costs, natural gas purchase costs, demand response costs, and grid access fees in the virtual power plant distributed trading model. Simultaneously, the day-ahead operation optimization model considers the revenue from electricity, heat, and cooling sales to internal users of the virtual power plant, as well as the revenue from distributed electricity trading and distributed green electricity trading in the virtual power plant distributed trading model. Constraints imposed on the day-ahead operation optimization model include: distributed trading constraints in the virtual power plant distributed trading model, as well as power balance constraints, transmission power constraints, demand response constraints, and equipment operation constraints.

[0016] S4. The alternating direction multiplier method is used to solve the day-ahead operation optimization model to obtain the day-ahead operation strategy of the virtual power plant for a total of 24 time periods from 07:00 on the operating day to 07:00 on the next day.

[0017] Furthermore, the mathematical expression for the distributed transaction revenue model in step S2 is as follows:

[0018] ,

[0019] ,

[0020] ,

[0021] In the formula: Revenue from distributed electricity trading in virtual power plants; Revenue from distributed green electricity trading in virtual power plants; The cost of grid connection fees for virtual power plants; , These refer to the ordinary electrical power supplied by virtual power plants to residential users that is transferable but not adjustable; and These refer to the green electricity power that can be transferred to residential users' loads but cannot be adjusted, supplied by virtual power plants; and These refer to the electricity prices when virtual power plants supply electricity to residential users' transferable and non-adjustable loads, respectively. This refers to the cost of internet access. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment The time span.

[0022] Furthermore, the modeling of distributed transaction constraints in step S2 is as follows:

[0023] 1) Energy interaction constraints between virtual power plants and transferable loads of residential users:

[0024] ,

[0025] ,

[0026] ,

[0027] ,

[0028] In the formula: In distributed transactions, the total amount of electricity that a virtual power plant needs to supply to residential users within period i is transferable. For period i, the maximum electricity consumption that a residential user can transfer load during period t;

[0029] 2) Energy interaction constraints between virtual power plants and unadjustable loads of residential users:

[0030] ,

[0031] ,

[0032] In the formula: The power consumption of non-adjustable loads for residential users;

[0033] 3) Renewable energy consumption constraints for virtual power plants supplying green electricity to residential users:

[0034] ,

[0035] Where: the minimum renewable energy consumption volume corresponding to the residential user consumption target. .

[0036] Furthermore, the objective function of the virtual power plant day-ahead operation optimization model is specifically expressed as follows:

[0037] ,

[0038] In the formula: C VPP Total operating cost of a virtual power plant; For equipment operation and maintenance costs; Cost of purchasing natural gas; Cost of responding to demand; The cost of grid connection fees for virtual power plants; Revenue generated from the sale of electricity, heat, and cooling to internal users by the virtual power plant; Revenue from distributed electricity trading in virtual power plants; Revenue from distributed green electricity trading by virtual power plants.

[0039] Furthermore, the calculation expressions for each cost or revenue in the virtual power plant day-ahead operation optimization model are as follows:

[0040] (1) Equipment operation and maintenance costs:

[0041] ,

[0042] In the formula: For equipment operation and maintenance costs; , , , , These are MT power generation, ES charging power, ES discharging power, WT power generation, and PV power generation, respectively. , , , , , and These are the heating power of GSHP, the heat release power of HS, the heat storage power of HS, the cooling power of AC, the cooling power of EC, the cold release power of CS, and the cold storage power of CS. , , , , , , , and The coefficients for the operation and maintenance costs of each piece of equipment; Total number of runtime segments ; runtime segment Time span;

[0043] (2) Natural gas purchase cost:

[0044] ,

[0045] In the formula: Cost of purchasing natural gas; The natural gas power consumed by MT; The lower heating value of natural gas, expressed in kWh / m³. 3 ; The price for purchasing natural gas;

[0046] (3) Demand response cost:

[0047] ,

[0048] In the formula: Cost of responding to demand; , and These are the virtual power plant load transfer price, power increment, and electricity experience loss compensation for time period t within period i (including multiple consecutive time periods); , and These include the virtual power plant's ability to reduce load dispatch prices, power reduction amounts, and compensation for losses in user experience. This represents the total number of transferable periods for the virtual power plant's transferable load. and These represent the first and last periods of period i;

[0049] (4) Network access fee cost:

[0050] ,

[0051] In the formula: The cost of grid connection fees for virtual power plants; for, These refer to the ordinary electrical power supplied by virtual power plants to residential users that is transferable but not adjustable; and These refer to the green electricity power that can be transferred to residential users' loads but cannot be adjusted, supplied by virtual power plants; This refers to the cost of internet access. The total number of transferable periods for residential users' transferable load; Total number of runtime segments ; runtime segment Time span;

[0052] (5) Revenue from the sale of electricity, heat, and cooling to internal users by the virtual power plant:

[0053] ,

[0054] In the formula: , , EU in HU in CU in The power consumption, heat consumption, and cooling consumption of the device; , and These are the prices for electricity, heat, and cooling sold by the virtual power plant to its internal users;

[0055] (6) Revenue from distributed electricity trading:

[0056] ,

[0057] In the formula: Revenue from distributed electricity trading in virtual power plants; , These refer to the ordinary electrical power supplied by virtual power plants to residential users for loads that can be transferred but are not adjustable. and These refer to the electricity prices when virtual power plants supply electricity to residential users' transferable and non-adjustable loads, respectively. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment Time span;

[0058] (7) Revenue from distributed green electricity trading:

[0059] ,

[0060] Revenue from distributed green electricity trading in virtual power plants; and These refer to the green electricity power that can be transferred to residential users' loads but cannot be adjusted, supplied by virtual power plants; and These refer to the electricity prices when virtual power plants supply electricity to residential users' transferable and non-adjustable loads, respectively. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment The time span.

[0061] The virtual power plant described in this invention includes an integrated energy system and distributed wind power. The integrated energy system comprises a micro gas turbine, distributed photovoltaic power, electric energy storage, a ground source heat pump, thermal storage devices, an absorption chiller, an electric compressor chiller, cold storage devices, and internal users of electrical, thermal, and cold energy. The day-ahead operation optimization cycle of the virtual power plant is from 07:00 on the operating day to 07:00 on the following day, totaling 24 time periods, each lasting one hour.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1) In distributed transactions, the method of this invention can determine electricity prices and achieve a reasonable distribution of distributed transaction revenue based on the supply and demand relationship between VPPs and residential users. When residential users do not provide VPPs with flexible adjustment capabilities, the electricity price is relatively high, and the revenue distribution is biased towards VPPs. When residential users provide VPPs with flexible adjustment capabilities, the electricity price will decrease, and the revenue distribution will be biased towards residential users.

[0064] 2) In addition to participating solely in centralized market transactions, the distributed trading strategy of this invention can further enhance the operating revenue and green, low-carbon characteristics of VPPs. Compared to participating only in centralized markets, VPPs that further participate in distributed trading experience a 10.04% increase in operating revenue, while wind and solar curtailment and carbon emissions from the distribution network are further reduced by 100% and 13.24%, respectively. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the VPP structure used in the specific embodiments of the present invention.

[0066] Figure 2 These are the maximum output prediction curves for distributed wind turbine (WT) and distributed photovoltaic (PV) power, as set in the example analysis of this invention.

[0067] Figure 3 This is the VPP power load prediction curve set in the example analysis of this invention.

[0068] Figure 4 This is the VPP heating and cooling load prediction curve set in the example analysis of this invention.

[0069] Figure 5 This is the load prediction curve for residential users in the distribution network set in the example analysis of this invention.

[0070] Figure 6 This is the VPP power trading strategy in scenario 1 of the calculation results of the example of this invention.

[0071] Figure 7 This is the VPP resource operation plan for scenario 1 in the calculation results of the example of this invention.

[0072] Figure 8 This is the VPP renewable energy generation and consumption plan for scenario 1 in the calculation results of this invention example.

[0073] Figure 9 This is the residential user electricity purchase strategy in scenario 1 of the calculation results of the example of this invention.

[0074] Figure 10 This is the electricity price in scenario 1 of the calculation results of this invention example.

[0075] Figure 11 This refers to the transferable load 1 power of residential users in scenario 1 of the calculation results of this invention example.

[0076] Figure 12 This refers to the transferable load 2 power of residential users in scenario 1 of the calculation results of this invention.

[0077] Figure 13 The power of the transferable load for residential users in scenario 1 is the power of the load calculated in the example of this invention. Detailed Implementation

[0078] To better understand the above-mentioned objectives, features, and advantages of the present invention, the technical solution of the present invention will be further described in detail below with reference to the specific implementation of the model.

[0079] A method for day-ahead operation of a virtual power plant in a distributed coupled market with residential users includes:

[0080] (1) Construct a trading framework for distributed power and distributed green electricity trading between virtual power plants and residential users within the distribution network. The residential users include two categories: transferable loads and non-adjustable loads, wherein:

[0081] The distributed electricity trading and distributed green electricity trading are carried out simultaneously in the same distributed coupled market;

[0082] The virtual power plant and residential users jointly determine the electricity price based on electricity supply and demand and flexible adjustment capabilities;

[0083] The virtual power plant interacts with both the transferable and non-adjustable loads of residential users, including:

[0084] For non-adjustable loads, the virtual power plant supplies them with electricity and ensures that their power consumption remains constant;

[0085] For transferable loads, the virtual power plant supplies them with electricity and utilizes its flexible adjustment capabilities to adjust their power consumption within the time frame allowed by residential users.

[0086] (2) Based on the transaction framework of step (1), construct a distributed transaction model for virtual power plants, including: modeling of distributed transaction revenue and modeling of distributed transaction constraints; the modeling of distributed transaction revenue includes: distributed electricity transaction revenue, distributed green electricity transaction revenue and grid access cost; the modeling of distributed transaction constraints includes: electricity interaction constraints between virtual power plants and transferable loads of residential users, electricity interaction constraints between virtual power plants and non-adjustable loads of residential users, and renewable energy consumption constraints for green electricity supplied by virtual power plants to residential users;

[0087] (3) Based on the virtual power plant distributed trading model constructed in step (2), establish a day-ahead operation optimization model for the virtual power plant under the distributed coupled market; the day-ahead operation optimization model takes the minimum total operating cost of the virtual power plant as the optimization objective, the total operating cost includes equipment operation and maintenance cost, natural gas purchase cost, demand response cost, and grid access cost in the virtual power plant distributed trading model; at the same time, the day-ahead operation optimization model considers the revenue of the virtual power plant from selling electricity, heat and cooling to internal users, as well as the revenue from distributed electricity trading and distributed green electricity trading in the virtual power plant distributed trading model; the constraints imposed on the day-ahead operation optimization model include: distributed trading constraints in the virtual power plant distributed trading model, as well as power balance constraints, transmission power constraints, demand response constraints and equipment operation constraints;

[0088] (4) The alternating direction multiplier method is used to solve the day-ahead operation optimization model to obtain the day-ahead operation strategy of the virtual power plant for a total of 24 time periods from 07:00 on the operating day to 07:00 on the next day.

[0089] The specific implementation steps are as follows:

[0090] Step 1: Detailed Implementation Description of the VPP Structure Used

[0091] This invention provides a detailed model of a VPP consisting of an integrated energy system (IES) and distributed wind turbines (WT). In the VPP, the IES and WT are connected to the distribution network from different nodes. The IES further includes a combined heat and power micro turbine (MT), distributed photovoltaic (PV), energy storage (ES), and internal energy users (EU).in Ground source heat pump (GSHP), heat storage (HS), and indoor heat user (HU) in Absorption chillers (AC), electric chillers (EC), cold storage (CS), and internal cold users (CU) in In summary, the specific structure of a VPP is as follows: Figure 1 As shown.

[0092] Step 2:

[0093] Modeling the objective function:

[0094] First, the optimization objective of this model is the total operating cost of the VPP. lowest:

[0095] ,

[0096] In the formula: For equipment operation and maintenance costs; Cost of purchasing natural gas; Cost of responding to demand; VPP network access fee cost; Revenue generated by the VPP from the sale of electricity, heat, and cooling to its internal users; Revenue from VPP distributed electricity trading; Revenue from VPP distributed green electricity trading.

[0097] Then, the calculation expressions for each cost or revenue in the objective function are as follows:

[0098] (1) Equipment operation and maintenance costs:

[0099] ,

[0100] In the formula: For equipment operation and maintenance costs; , , , , These are MT power generation, ES charging power, ES discharging power, WT power generation, and PV power generation, respectively. , , , , , and These are the heating power of GSHP, the heat release power of HS, the heat storage power of HS, the cooling power of AC, the cooling power of EC, the cold release power of CS, and the cold storage power of CS. , , , , , , , and The coefficients for the operation and maintenance costs of each piece of equipment; This represents the total number of runtime segments. Unless otherwise specified in the text, the default runtime segments are [number of segments]. ; runtime segment The time span.

[0101] (2) Natural gas purchase cost:

[0102] ,

[0103] In the formula: Cost of purchasing natural gas; The natural gas power consumed by MT; The lower heating value of natural gas, expressed in kWh / m³. 3 ; This refers to the purchase price of natural gas.

[0104] (3) Demand response cost:

[0105] ,

[0106] In the formula: Cost of responding to demand; , and These are the VPP transferable load dispatch price, power increment, and power experience loss compensation for time period t within period i (including multiple consecutive time periods); , and These are the VPP's ability to reduce load dispatch price, power reduction amount, and compensation for power experience loss; This represents the total number of transferable periods for the transferable load of the VPP. and These represent the first and last periods of period i, and there may be overlapping periods between different periods.

[0107] (4) Network access fee cost:

[0108] ,

[0109] In the formula: VPP network access fee cost; For period i (including multiple consecutive periods), the ordinary electrical power supplied by VPP to residential users that can be transferred to the load is: The ordinary electrical power supplied by the VPP to the non-adjustable loads of residential users; and These refer to the green electricity power that VPPs can transfer to residential users' non-adjustable loads; This refers to the cost of internet access. The total number of transferable periods for residential users' transferable load; Total number of runtime segments ; runtime segment The time span.

[0110] (5) Revenue from the sale of electricity, heat, and cooling to internal users by the VPP:

[0111] ,

[0112] In the formula: , , EU in HU in CU in The power consumption, heat consumption, and cooling consumption of the device; , and These are the prices that the VPP sells to its internal users for electricity, heat, and cooling, respectively.

[0113] (6) Revenue from distributed electricity trading:

[0114] ,

[0115] In the formula: Revenue from VPP distributed electricity trading; , These refer to the ordinary electrical power supplied by the VPP to residential users for loads that can be transferred but are not adjustable. and These are the electricity prices when a VPP supplies electricity to residential users with transferable and non-adjustable loads, respectively. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment The time span.

[0116] (7) Revenue from distributed green electricity trading:

[0117] ,

[0118] Revenue from VPP distributed green electricity trading; and These refer to the green electricity power that VPPs can transfer to residential users' non-adjustable loads; and These are the electricity prices when a VPP supplies electricity to residential users with transferable and non-adjustable loads, respectively. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment The time span.

[0119] In distributed green electricity trading, the price of green electricity includes the price of green electricity volume and the price of green electricity environmental rights, and the price of green electricity environmental rights in the distributed market is consistent with that in the centralized market.

[0120] Step 3: Model the operational constraints

[0121] (1) VPP distributed transaction constraints:

[0122] 1) Constrain the electrical energy exchange process between VPP and residential users' transferable load:

[0123] ,

[0124] ,

[0125] ,

[0126] ,

[0127] In the formula: In distributed transactions, the VPP needs to supply the total amount of electricity that can be transferred to residential users within period i; Let be the maximum electricity consumption of residential users' transferable load during time period t within period i. For general transferable loads with flexible electricity transfer, such as water heaters and washing machines, their maximum electricity consumption in each time period is not restricted, and let be... It should be a sufficiently large positive number. For electric vehicle loads, to ensure charging safety, their maximum power consumption at different times needs to be limited.

[0128] 2) Constrain the electrical energy exchange process between VPP and the non-adjustable load of residential users:

[0129] ,

[0130] ,

[0131] In the formula: The power consumption of non-adjustable loads for residential users.

[0132] 3) Due to cost considerations, residential users only complete the basic consumption tasks, therefore the minimum renewable energy consumption volume corresponding to the residential users' consumption tasks is taken into account. Constraints are imposed on the amount of green electricity supplied by VPPs to residential users:

[0133] ,

[0134] (2) Modeling other operational constraints: including commonly used power balance constraints, transmission power constraints, and demand response constraints. The specific constraint formulas are as follows:

[0135] 1) VPP power balance constraint:

[0136] ,

[0137] ,

[0138] ,

[0139] ,

[0140] ,

[0141] ,

[0142] In the formula: and These are the power consumption figures for GSHP and EC, respectively. This represents the total electrical power sold by the VPP in day-to-day centralized electricity and green electricity trading. This represents the total power purchased by the VPP in day-to-day centralized power and green electricity trading. and These refer to the ordinary electrical power sold and purchased by VPP, respectively. The green electricity output of VPP refers to the green electricity output sold by VPP in centralized green electricity trading. Green electricity purchased for VPP; A variable between 0 and 1, where a value of 1 indicates that the VPP sells ordinary electricity and green electricity in day-ahead centralized electricity and green electricity trading, respectively; M is a positive number with a very large absolute value;

[0143] 2) VPP transmission power constraints:

[0144] ,

[0145] ,

[0146] In the formula: This represents the maximum transmission power between the IES in the VPP and the distribution network. This represents the maximum transmission power between the distribution network and the upstream transmission network. This refers to the power consumption of residential users in the power distribution network.

[0147] 3) VPP demand response constraints:

[0148] The loads of internal power users within the VPP are categorized into transferable, reduceable, and non-adjustable loads. On the one hand, the electricity demand of transferable loads must be met. On the other hand, a portion of commercial lighting loads are classified as reduceable loads so that reducing load demand does not significantly impact the electricity usage plans of internal power users.

[0149] ,

[0150] In the formula: , and These are the total power of the transferable load of the VPP, the power of the load that can be reduced by the VPP, and the power of the non-adjustable load of the VPP, respectively.

[0151] The following constraints are imposed on the transferable load power and the load power that can be reduced by the VPP:

[0152] ,

[0153] ,

[0154] ,

[0155] ,

[0156] ,

[0157] ,

[0158] ,

[0159] ,

[0160] ,

[0161] ,

[0162] In the formula: VPP transferable load power; This represents the predicted power of the transferable load of the VPP. This represents the reduction in transferable load power of the VPP. This represents the upper limit of the transferable load electricity consumption of VPP during time period t; This is a multiple of the maximum power consumption of the transferable load of the VPP; VPP can reduce the predicted load power.

[0163] The other operational constraints mentioned above are not part of the core innovation of this invention and have been widely used in existing research. Therefore, they are listed above to meet the requirement of full disclosure.

[0164] Step 4: Solving the model

[0165] The model constructed in this invention includes a distributed transaction model. The alternating direction multiplier method is used to solve the model to obtain the day-ahead operation strategy of the virtual power plant for a total of 24 time periods from 07:00 on the operating day to 07:00 on the next day.

[0166] Case Analysis

[0167] To demonstrate the effectiveness of the method of this invention, both centralized and distributed coupled markets were considered in the numerical example analysis to reflect the improvement of VPP operating revenue and green and low-carbon performance of this invention in the distributed coupled market.

[0168] (1) Detailed description of the example model

[0169] This invention uses a VPP under a certain distribution network as a case study model, the structure of which is shown below. Figure 1 Specifically: 1) The VPP includes IES and WT, which are connected to the distribution network from different busbars. There is a tie line between IES and the distribution network, and IES includes MT, PV, ES, and EU. in GSHP, HS, HU in AC, EC, CS and CU in 2) In addition to VPPs, there are also several residential users in the distribution network. 3) There are also tie lines between the distribution network and the upstream transmission network.

[0170] (2) Parameter settings

[0171] 1) Overview of overall parameter settings:

[0172] To fully demonstrate the supporting role of mature business models in VPP operation, this invention, referencing Europe where VPP development is most mature, generally enables VPPs to have strong external power generation capabilities, thus ensuring their profitability.

[0173] 2) Carbon-Green Certificate-Green Electricity Trading Parameters:

[0174] Table 1 Carbon-Green Certificate-Green Electricity Trading Parameters

[0175]

[0176] 3) Energy trading prices:

[0177] Table 2 Energy Trading Prices

[0178]

[0179] 4) MT parameter settings:

[0180] Table 3 MT Basic Parameters

[0181]

[0182] 5) Other equipment parameters:

[0183] Table 4 Energy Storage Equipment Parameters

[0184]

[0185] Table 5 Other Equipment Parameters

[0186]

[0187] 6) VPP adjustable load parameters:

[0188] Table 6 VPP Transferable Load Parameters

[0189]

[0190] In addition, the VPP can reduce load at a price of 0.18 yuan / kWh, and will no longer be listed separately.

[0191] 7) Transmission line parameters:

[0192] Table 7 Transmission Line Parameters

[0193]

[0194] 8) Residential user load parameters in the distribution network:

[0195] Table 8 Parameters of Transferable Load for Residential Users

[0196]

[0197] 9) Current forecast curves for maximum wind and solar power: see Figure 2 .

[0198] 10) VPP Internal User Load Forecast Curve: See the VPP internal user load forecast curve. Figure 3 The predicted load curves for heat and cold energy users inside the VPP are shown below. Figure 4 .

[0199] 11) Residential user load forecast curves in the distribution network: see Figure 5

[0200] 12) VPP operation optimization cycle:

[0201] To ensure continuous scheduling of transferable loads, this invention sets the VPP operation optimization cycle to 07:00 on the operating day (D day) to 07:00 on the next day, and divides this cycle into 24 time periods, each lasting 1 hour. This cycle will not affect the VPP's actual participation in day-ahead centralized electricity and green electricity trading. For example, if a day-ahead centralized electricity trading requires the VPP to submit its trading strategy for operating day D on day D-1, then the VPP can simply merge and submit its trading strategy for day D (00:00-07:00) obtained on day D-2 with its trading strategy for day D (07:00-24:00) obtained on day D-1.

[0202] (3) Scenario setting

[0203] The following three scenarios are set up as shown in Table 9:

[0204] Table 9 Scenario Setting

[0205]

[0206] 1) Scenario 1 simultaneously adopts the VPP day-ahead operation strategy under a centralized market, and the VPP day-ahead operation strategy under a distributed coupled market constructed in this invention.

[0207] 2) Scenario 2 only adopts the VPP day-ahead operation strategy under a centralized market, without taking into account distributed transactions.

[0208] 3) Scenario 3, based on Scenario 2, further considers distributed electricity trading.

[0209] (4) Operation strategy display

[0210] The VPP operation strategy in Scenario 1 is demonstrated to lay the foundation for in-depth analysis later. The demonstrated content includes: 1) VPP trading strategies under coupled markets, such as... Figure 6 As shown in Table 10, a positive power value in the graph indicates the power sold, while a negative value indicates the power purchased, and the same applies below; 2) VPP resource operation plan, such as Figure 7 As shown in the figure, a positive power value indicates power generation, and a negative power value indicates power consumption, and the same applies below; 3) VPP renewable energy power generation and consumption plan, such as Figure 8 As shown, renewable energy units include PV and WT; 4) Electricity purchase strategies for residential users, such as Figure 9 The image shown is for supplementary explanation.

[0211] Table 10 Carbon-Green Certificate Trading Strategies for VPPs in Scenario 1

[0212]

[0213] As shown in the attached figures and table above, VPP participates in centralized green electricity trading, carbon-green certificate trading, distributed electricity trading, and distributed green electricity trading. Based on the trading strategy, it formulates operation plans for internal resources such as MT, PV, WT, and ES, as well as specific renewable energy power generation and consumption plans.

[0214] Figure 6 Two points are noteworthy in Table 10: First, after participating in distributed energy trading, the VPP did not participate in centralized energy trading, indicating that the VPP and residential users had sufficient interaction regarding ordinary electricity in the distribution network. Second, the VPP participated in both green electricity and green certificate trading, suggesting that the VPP's renewable energy generation is relatively abundant. In addition to participating in green electricity trading, it can also sell excess grid-connected renewable energy in distributed energy trading and sell corresponding tradable green certificates in green certificate trading.

[0215] Furthermore, the role of centralized electricity trading in providing a safety net for VPP operation strategies remains significant. For example... Figure 9 As shown, residential users can indirectly participate in centralized power trading through power grid companies to supplement their ordinary electricity needs. Therefore, centralized power trading provides a basic guarantee for the power balance of the distribution network. Consequently, centralized power trading reduces the restrictions on VPP construction and operation strategies.

[0216] (5) Analysis of the rationality of distributed transaction prices

[0217] Based on the results of Scenario 1, this paper focuses on analyzing the electricity price in distributed transactions to demonstrate that the distributed transaction model proposed in this invention can form a reasonable distributed transaction price and achieve a reasonable distribution of distributed transaction revenue. The following content is presented for analysis: 1) The electricity price in Scenario 1, such as... Figure 10 As shown; 2) The power curves of transferable load 1 to transferable load 3 for residential users in scenario 1, as shown. Figure 11 — Figure 13 As shown.

[0218] Depend on Figure 10 — Figure 13It is evident that the electricity price under the distributed trading model of this invention can fully reflect the supply and demand relationship between VPPs and residential users in terms of electricity and flexibility, and rationally allocate distributed trading revenue. Specifically:

[0219] 1) In distributed transactions, when residential users do not provide flexible adjustment capabilities to the VPP, electricity prices are relatively high, and revenue distribution favors the VPP. On the one hand, combined with... Figure 11 It can be seen that the non-adjustable load and transferable load 1 of residential users do not provide flexible adjustment capabilities to the VPP. On the other hand, such as Figure 10 As shown, the electricity price between VPP and the non-adjustable load of residential users, as well as the electricity price between VPP and the transferable load 1 of residential users, are both higher than the centralized market electricity price, but do not exceed the price at which residential users purchase electricity from the power grid company.

[0220] 2) In distributed transactions, when residential users provide flexible adjustment capabilities to the VPP, electricity prices decrease, and revenue distribution favors residential users. On the one hand, such as... Figure 12 and Figure 13 As shown, after coordination with the VPP, the transferable loads 2 and 3 of residential users transferred part of their electricity consumption from 18:00-19:00 and 18:00-24:00 to 23:00-24:00 and 24:00-7:00 the next day, respectively. On the other hand, as... Figure 10 As shown, the electricity price between VPP and residential user transferable load 2, and between VPP and residential user transferable load 3, decreased significantly between 23:00-24:00 and 24:00-7:00 the next day, respectively, and the price was lower than the electricity sales price in the centralized market.

[0221] (6) Effectiveness analysis of distributed trading strategies

[0222] To verify that the VPP day-ahead operation strategy under the distributed coupled market of this invention can further improve the operating revenue and green and low-carbon characteristics of VPP through distributed trading, the operating results of scenarios 1-3 are compared and analyzed. The VPP operating results in each scenario are shown in Table 11:

[0223] Table 11 VPP running results under various scenarios

[0224]

[0225] 1) As shown in Scenario 3, the economic viability and green, low-carbon characteristics of VPPs are further enhanced after taking into account distributed energy trading. Table 3-9 shows that in Scenario 3, the VPP's operating revenue increases by 8.78% compared to Scenario 2 by utilizing the distributed energy trading strategy. Simultaneously, under the distributed energy trading model in Scenario 3, the VPP's wind and solar curtailment and the carbon emissions from its distribution network are reduced by 100% and 13.24%, respectively, compared to Scenario 2.

[0226] 2) As shown in Scenario 1, further incorporating distributed green electricity trading into distributed transactions can further enhance VPP operating revenue. Referring to Table 11, compared to Scenario 3, Scenario 1, by including distributed green electricity trading, allows VPPs to gain additional revenue while maintaining their green and low-carbon characteristics. Specifically, VPP operating revenue increases by 1.16%. Compared to Scenario 2, the total VPP operating revenue increases by 10.04%.

Claims

1. A method for day-ahead operation of a virtual power plant in a distributed coupled market with residential users, characterized in that, Includes the following steps: S1. Establish a trading framework for distributed energy trading and distributed green energy trading between virtual power plants and residential users within the distribution network. The residential users include two categories: transferable loads and non-adjustable loads. The distributed electricity trading and distributed green electricity trading are carried out simultaneously in the same distributed coupled market; The virtual power plant and residential users jointly determine the electricity price based on electricity supply and demand and flexible adjustment capabilities; The virtual power plant interacts with both the transferable and non-adjustable loads of residential users, including: For non-adjustable loads, the virtual power plant supplies them with electricity and ensures that their power consumption remains constant; For transferable loads, the virtual power plant supplies them with electricity and utilizes its flexible adjustment capabilities to adjust their power consumption within the time frame allowed by residential users. S2. Based on the transaction framework of step S1, construct a distributed transaction model for virtual power plants, including: modeling of distributed transaction revenue and modeling of distributed transaction constraints; the modeling of distributed transaction revenue includes: distributed electricity transaction revenue, distributed green electricity transaction revenue, and grid access cost; the modeling of distributed transaction constraints includes: electricity interaction constraints between virtual power plants and transferable loads of residential users, electricity interaction constraints between virtual power plants and non-adjustable loads of residential users, and renewable energy consumption constraints for green electricity supplied by virtual power plants to residential users; S3. Based on the virtual power plant distributed trading model constructed in step S2, establish a day-ahead operation optimization model for the virtual power plant under a distributed coupled market. The day-ahead operation optimization model aims to minimize the total operating cost of the virtual power plant, which includes equipment operation and maintenance costs, natural gas purchase costs, demand response costs, and grid access fees in the virtual power plant distributed trading model. Simultaneously, the day-ahead operation optimization model considers the revenue from electricity, heat, and cooling sales to internal users of the virtual power plant, as well as the revenue from distributed electricity trading and distributed green electricity trading in the virtual power plant distributed trading model. Constraints imposed on the day-ahead operation optimization model include: distributed trading constraints in the virtual power plant distributed trading model, as well as power balance constraints, transmission power constraints, demand response constraints, and equipment operation constraints. S4. The alternating direction multiplier method is used to solve the day-ahead operation optimization model to obtain the day-ahead operation strategy of the virtual power plant for a total of 24 time periods from 07:00 on the operating day to 07:00 on the next day.

2. The method according to claim 1, characterized in that, The mathematical expression for the distributed transaction revenue model in step S2 is as follows: ， ， ， In the formula: Revenue from distributed electricity trading in virtual power plants; Revenue from distributed green electricity trading in virtual power plants; The cost of grid connection fees for virtual power plants; , These refer to the ordinary electrical power supplied by virtual power plants to residential users that is transferable but not adjustable; and These refer to the green electricity power that can be transferred to residential users' loads but cannot be adjusted, supplied by virtual power plants; and These refer to the electricity prices when virtual power plants supply electricity to residential users' transferable and non-adjustable loads, respectively. This refers to the cost of internet access. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment The time span.

3. The method according to claim 1, characterized in that, The modeling of distributed transaction constraints in step S2 is as follows: 1) Energy interaction constraints between virtual power plants and transferable loads of residential users: ， ， ， ， In the formula: In distributed transactions, the total amount of electricity that a virtual power plant needs to supply to residential users within period i is transferable. For period i, the maximum electricity consumption that a residential user can transfer load during period t; 2) Energy interaction constraints between virtual power plants and unadjustable loads of residential users: ， ， In the formula: The power consumption of non-adjustable loads for residential users; 3) Renewable energy consumption constraints for virtual power plants supplying green electricity to residential users: ， Where: the minimum renewable energy consumption volume corresponding to the residential user consumption target. .

4. The method according to claim 1, characterized in that, The objective function of the virtual power plant day-ahead operation optimization model is specifically expressed as follows: ， In the formula: C VPP Total operating cost of a virtual power plant; For equipment operation and maintenance costs; Cost of purchasing natural gas; Cost of responding to demand; The cost of grid connection fees for virtual power plants; Revenue generated from the sale of electricity, heat, and cooling to internal users by the virtual power plant; Revenue from distributed electricity trading in virtual power plants; Revenue from distributed green electricity trading by virtual power plants.

5. The method according to claim 4, characterized in that, The calculation expressions for each cost or revenue in the day-ahead operation optimization model of the virtual power plant are as follows: (1) Equipment operation and maintenance costs: ， In the formula: For equipment operation and maintenance costs; , , , , These are MT power generation, ES charging power, ES discharging power, WT power generation, and PV power generation, respectively. , , , , , and These are the heating power of GSHP, the heat release power of HS, the heat storage power of HS, the cooling power of AC, the cooling power of EC, the cold release power of CS, and the cold storage power of CS. , , , , , , , and The coefficients for the operation and maintenance costs of each piece of equipment; Total number of runtime segments ; runtime segment Time span; (2) Natural gas purchase cost: ， In the formula: Cost of purchasing natural gas; The natural gas power consumed by MT; The lower heating value of natural gas, expressed in kWh / m³. 3 ; The price for purchasing natural gas; (3) Demand response cost: ， In the formula: Cost of responding to demand; , and These are the virtual power plant load transfer price, power increment, and electricity experience loss compensation for time period t within period i (including multiple consecutive time periods); , and These include the virtual power plant's ability to reduce load dispatch prices, power reduction amounts, and compensation for losses in user experience. This represents the total number of transferable periods for the virtual power plant's transferable load. and These represent the first and last periods of period i; (4) Network access fee cost: ， In the formula: The cost of grid connection fees for virtual power plants; for, These refer to the ordinary electrical power supplied by virtual power plants to residential users that is transferable but not adjustable; and These refer to the green electricity power that can be transferred to residential users' loads but cannot be adjusted, supplied by virtual power plants; This refers to the cost of internet access. The total number of transferable periods for residential users' transferable load; Total number of runtime segments ; runtime segment Time span; (5) Revenue from the sale of electricity, heat, and cooling to internal users by the virtual power plant: ， In the formula: , , EU in HU in CU in The power consumption, heat consumption, and cooling consumption of the device; , and These are the prices for electricity, heat, and cooling sold by the virtual power plant to its internal users; (6) Revenue from distributed electricity trading: ， In the formula: Revenue from distributed electricity trading in virtual power plants; , These refer to the ordinary electrical power supplied by virtual power plants to residential users for loads that can be transferred but are not adjustable. and These refer to the electricity prices when virtual power plants supply electricity to residential users' transferable and non-adjustable loads, respectively. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment Time span; (7) Revenue from distributed green electricity transactions: ， Revenue from distributed green electricity trading in virtual power plants; and These refer to the green electricity power that can be transferred to residential users' loads but cannot be adjusted, supplied by virtual power plants; and These refer to the electricity prices when virtual power plants supply electricity to residential users' transferable and non-adjustable loads, respectively. The price of environmental rights for green electricity; The total number of transferable periods for residential users' transferable load; and These represent the first and last periods of period i, respectively; Total number of runtime segments ; runtime segment The time span.

6. The method according to any one of claims 1-5, characterized in that, The virtual power plant includes an integrated energy system and distributed wind power. The integrated energy system includes micro gas turbines, distributed photovoltaics, electric energy storage, ground source heat pumps, thermal storage devices, absorption chillers, electric compressor chillers, cold storage devices, and internal users of electrical, thermal, and cold energy.

7. The method according to any one of claims 1-5, characterized in that, The virtual power plant's day-ahead operation optimization cycle is from 07:00 on the operating day to 07:00 on the next day, totaling 24 time periods, each lasting 1 hour.

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