Intelligent power distribution network peak regulation market transaction optimization method considering flexibility evaluation

Abstract

The invention discloses an intelligent power distribution network peak regulation market transaction optimization method considering flexibility evaluation, and the method comprises the steps: building an intelligent power distribution network flexibility evaluation model, operating the flexibility evaluation model, and obtaining upward and downward flexibility supply index values of various flexible load resources; establishing an intelligent power distribution network peak regulation market transaction optimization model, taking peak regulation market income maximization and consideration of satisfaction income and peak-valley difference penalty as a target function, and taking the obtained upward and downward flexible supply index values as constraints; updating iteration is carried out, an objective function of the flexibility evaluation model is updated for the obtained peak regulation market income, a power balance constraint is added on the basis of a flexible load operation constraint, and upward and downward flexibility supply values of the intelligent power distribution network capable of participating in the peak regulation market are calculated; and outputting the obtained flexible supply values of the temperature control load, the electric vehicle and the energy storage system in each time period until an iteration condition is met, and using the flexible supply values for peak regulation of a superior power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for optimizing transactions in a peak-shaving market of an intelligent distribution network taking flexibility evaluation into account. Background Art

[0002] With the continuous increase in installed capacity of new energy power generation, the contradiction of insufficient flexibility regulation capacity of the power system has become increasingly prominent. Smart distribution networks contain flexible load resources such as energy storage systems, temperature control loads, and electric vehicles, which can provide flexibility support for the power system. However, the existing technology fails to fully consider the flexibility supply capacity and market benefits of these flexible load resources when the smart distribution network participates in peak-shaving market transactions, resulting in poor peak-shaving benefits and peak-shaving effects.

[0003] In order to solve the above problems, it is necessary to establish a new transaction optimization method for the peak-shaving market of smart distribution networks, which can take into account flexibility evaluation, so as to maximize the benefits of smart distribution networks when participating in the peak-shaving market and effectively reduce the load fluctuation of the external power grid. This method mainly consists of a transaction optimization model for the peak-shaving market of smart distribution networks and a flexibility evaluation model for smart distribution networks taking into account market benefits, which are coordinated through market benefits and flexibility supply variables.

[0004] The transaction optimization model of the peak-shaving market of the smart distribution network participates in the peak-shaving market with the goal of maximizing the peak-shaving revenue, satisfaction revenue and the peak-shaving effect revenue of the upper distribution network, including the flexible load models and constraints such as energy storage system, temperature control load, electric vehicle, etc. The flexibility evaluation model of the smart distribution network taking into account the market revenue takes the lowest overall internal operating cost of the smart distribution network as the objective function, including the flexible load models and constraints such as energy storage system, temperature control load, electric vehicle, etc., and calculates the flexibility supply of each flexible load resource under the premise of satisfying local consumption.

[0005] CN117977538A discloses a method for evaluating the flexibility of a power system. The method obtains original operating data of new energy units and time-series loads in the power system, and analyzes the original operating data according to a pre-configured output model to obtain the output of the new energy units and the output of flexible resources. The power system is analyzed according to the output of the new energy units and the output of flexible resources to determine the daily output of the new energy units and the surplus of the new energy units under the daily operating state of the power system, as well as the daily output of the flexible resources and the surplus of the flexible resources. The flexibility evaluation index value is calculated according to these data, and the flexibility of the power system is evaluated according to the flexibility evaluation index value.

[0006] At present, the defects of load user response satisfaction are generally ignored in market transaction research. It is necessary to ensure that the actual experience and needs of the user side are fully considered in the transaction process to enhance the enthusiasm and stability of user participation. Summary of the invention

[0007] In order to solve the above-mentioned technical problems existing in the background technology, the present invention discloses a smart distribution network peak-shaving market transaction optimization method taking flexibility evaluation into account, the optimization method mainly consists of a smart distribution network peak-shaving market transaction optimization model and a smart distribution network flexibility evaluation model taking market revenue into account, the two of which enable the smart distribution network peak-shaving market transaction optimization model and the flexibility evaluation model to achieve synergy through market revenue and flexibility supply variables, thereby maximizing the benefits of the smart distribution network operating entity, user satisfaction benefits and peak-shaving effect benefits of the superior distribution network, and effectively improving the overall operating efficiency and economic benefits of the smart distribution network in the power market.

[0008] A method for optimizing transactions in the peak load regulation market of smart distribution networks taking flexibility evaluation into account, including

[0009] Step 1: Establish an operation model for the flexible load of the smart distribution network, including building an energy storage system operation model, a temperature control load operation model, and an electric vehicle operation model;

[0010] Step 2: Establish a flexibility evaluation model for the smart distribution network; the model takes the lowest overall internal operating cost of the smart distribution network as the objective function, and takes the flexible load resource operation constraints and power balance constraints as the constraints, where the flexible load resource operation constraints include energy storage system constraints, temperature control load constraints, and electric vehicle charging and discharging constraints;

[0011] Step 3: Based on the established flexible load resource operation constraints and power balance constraints, run the flexibility evaluation model to obtain the upward and downward flexibility supply index values ​​of various flexible load resources;

[0012] Step 4: Establish a transaction optimization model for the peak-shaving market of the smart distribution network, with the peak-shaving market revenue maximization as the objective function while taking into account the satisfaction revenue and the peak-valley difference penalty, and the upward and downward flexibility supply index values ​​obtained in step 3 as constraints;

[0013] Step 5: Feed the peak-shaving market revenue obtained in step 4 back to step 2 to update the objective function of the smart distribution network flexibility evaluation model;

[0014] Repeat steps 2 to 4 to perform iterations until the difference between the results of the current and previous iterations is less than the preset value, then end the iteration and output the flexibility supply values ​​of the temperature-controlled loads, electric vehicles, and energy storage systems in each time period for peak load regulation of the upper-level distribution network.

[0015] Furthermore, the step 2 specifically includes:

[0016] The objective function is to minimize the overall internal operating cost of the smart distribution network, where the operating cost takes into account the benefits obtained by the smart distribution network participating in the peak-shaving market. The operating cost objective function taking into account the market benefits is shown as follows:

[0017]

[0018] In the formula The operating cost of the energy storage system; is the operating cost of the temperature control load; is the operating cost of electric vehicles; C loss is the network loss cost, c loss is the unit average operating cost of generators and flexible load resources, P loss is the active power loss of the smart distribution network, and the two together constitute the network loss cost; c E 、c W 、c V are the unit operating costs of the energy storage system, temperature control load, and electric vehicles to maintain the internal operation of the smart grid; C profit The income obtained by participating in the peak-shaving market comes from solving the peak-shaving market transaction optimization model. During initial operation, this parameter is set to 0.

[0019] Power balance constraints are added on the basis of flexible load operation constraints, including:

[0020]

[0021] Where P g P is the output sequence of the generator in the smart distribution network; PV , P W They are the photovoltaic and wind turbine output sequences respectively; Charge and discharge power sequence for energy storage system; is the charging and discharging power sequence of the i-th electric vehicle; P w is the temperature control load power sequence; P load Other load sequences for smart distribution network;

[0022] Furthermore, step three specifically includes:

[0023] S31: Establish energy storage system constraints, temperature control load constraints, and electric vehicle charging and discharging constraints; during initial operation, based on the operation model of the flexible load established in step 1, determine the energy storage system charging and discharging power, the temperature control load power, and the electric vehicle charging and discharging power respectively;

[0024] S32: Use the optimization solution method to simulate the objective function of the distribution network flexibility assessment model under the constraints set by S31. If any parameter does not meet the constraints set by S31, return to step S31 and readjust until the optimal solution that meets all the constraints set in step S31 is found, and the upward and downward flexibility supply index values ​​of various types of flexible load resources are obtained.

[0025] Furthermore, step four specifically includes:

[0026] S41: Establishing a smart distribution network peak-shaving market transaction optimization model with the peak-shaving market revenue maximization and taking into account the satisfaction revenue and the peak-valley difference penalty as the objective function, the objective function is as follows;

[0027]

[0028] Among them C s is the satisfaction benefit brought by the temperature control load and the electric vehicle. The specific calculation method is shown in the following formula (26) and formula (27); C profit Generate peak load revenue for smart distribution grid; To participate in the peak load response; and is the cost of energy storage system, temperature control load, and electric vehicles participating in the peak load regulation of the upper distribution network; C peak-vally is the peak-to-valley difference penalty cost caused by the peak-to-valley difference; L peak-vally is the peak-to-valley difference of the upper distribution network; α, β, δ and c peak-vally are respectively the user satisfaction coefficient of electric vehicle, temperature control load, peak-shaving profit coefficient of intelligent distribution network and unit penalty cost of peak-valley difference; since the cost is affected by many factors, the present invention calculates the peak-shaving cost C of the upper distribution network period by statistical analysis total The total peak-to-valley difference L total-peak The parameter is determined by the ratio of and They are the satisfaction of electric vehicle and temperature control load users respectively;

[0029] S42: Taking the upward and downward flexibility supply index values ​​of the smart distribution network in step 3 as constraints, the market transaction structure is obtained, as shown below:

[0030]

[0031]

[0032] in It provides index values ​​for the upward and downward flexibility of the smart distribution network to the external distribution network, and the flexibility supply index values ​​will be obtained by the flexibility evaluation model. and Both are Boolean variables, which are used to ensure that the smart distribution network cannot participate in peak shaving and valley filling at the same time.

[0033] Furthermore, in the iteration process, the energy storage system charging and discharging power, the temperature control load power, and the electric vehicle charging and discharging power in step S31 are iteratively updated according to the peak-shaving market revenue obtained in the previous calculation process;

[0034] Under the premise of satisfying local consumption, the energy storage system provides upward and downward flexibility by changing the charging and discharging power. The energy storage system flexibility supply model is shown in the following formula:

[0035]

[0036] In the formula They are the upward and downward flexibility supplies provided by the energy storage system at time t respectively;

[0037] The temperature control load is provided with upward and downward flexibility by changing the temperature change range set by the user. The temperature control load flexibility supply model is shown in the following formula:

[0038]

[0039] In the formula, Provide index values ​​for the upward and downward flexibility provided by the temperature control load at time t;

[0040] Electric vehicles provide flexibility by changing the charging and discharging power, while taking into account the time when electric vehicles are connected to the grid / off the grid and the need to meet the travel needs of electric vehicle users. The electric vehicle flexibility supply model is shown in the following formula:

[0041]

[0042] In the formula is the upward and downward flexibility supply of the ith electric vehicle in the tth period; t = lea represents the off-grid time;

[0043] After the smart distribution network itself is running, the output change that can still be provided by the flexible load resources such as energy storage systems, temperature control loads, and electric vehicles in the smart distribution network constitutes the upper and lower limits of the flexibility of the smart distribution network to participate in the peak-shaving market. The flexibility supply model of the smart distribution network is shown in the following formula:

[0044]

[0045] In the formula Provide upward and downward flexibility for the smart distribution network to the external distribution network.

[0046] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0047] 1. The optimization method can fully consider the flexible supply capacity of flexible load resources such as energy storage systems, temperature control loads, and electric vehicles in the smart distribution network, thereby improving the peak load regulation benefits and peak load regulation effects;

[0048] 2. By introducing market revenue and flexibility supply variables, the smart distribution network peak-shaving market transaction optimization model and flexibility evaluation model are coordinated, which improves the accuracy and comprehensiveness of the model;

[0049] 3. The actual operating characteristics of flexible load resources such as energy storage systems, temperature control loads, and electric vehicles are fully considered during modeling to reduce the deviation between the model and the actual situation; and by iteratively solving the smart distribution network peak-shaving market transaction optimization model and flexibility evaluation model, the benefits of the smart distribution network when participating in the peak-shaving market are maximized, and the load fluctuations of the external power grid are effectively reduced.

[0050] 4. The optimization method improves the existing flexibility evaluation method, which is not suitable for the local consumption characteristics of the smart distribution network and the current situation of being out of touch with market transactions. A flexibility evaluation mechanism is established that is closely integrated with the operating characteristics of the smart distribution network and deeply integrated with market transactions. It can accurately and objectively reflect the real flexibility supply capacity of the smart distribution network in market transactions and provide a solid foundation for reasonable market transaction decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of a method for optimizing transactions in a peak-shaving market of a smart distribution network taking flexibility evaluation into account in an embodiment;

[0052] Figure 2 It is a structural schematic diagram of two smart distribution networks with different structures providing services to a superior distribution network in a smart distribution network peak load market transaction optimization method taking flexibility evaluation into account in an embodiment;

[0053] Figure 3 The topology diagram of the IEEE33 node distribution network used is shown;

[0054] Figure 4 A schematic diagram showing the flexibility evaluation results of distributed smart distribution networks considering the peak-shaving market benefits;

[0055] Figure 5 A schematic diagram showing the flexibility evaluation results of distributed smart distribution networks without considering the peak load market benefits;

[0056] Figure 6 A comparison of market participation in smart distribution grids with and without flexibility assessment;

[0057] Figure 7 A comparison chart showing the peak load regulation results of the upper power grid with and without considering flexibility assessment is shown. DETAILED DESCRIPTION

[0058] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The described specific embodiments are only used to explain the present invention and are not intended to limit the present invention.

[0059] Reference will now be made in detail to specific embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Although the present invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the present invention to the embodiments described. On the contrary, it is intended to cover changes, modifications and equivalents included in the spirit and scope of the present invention as defined by the appended claims. It should be noted that the method steps described herein can all be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of the two.

[0060] In the description of the following embodiments, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "set" should be understood in a broad sense, for example, it can be fixedly connected or set, or it can be detachably connected or set, or connected or set in one piece. For ordinary technicians in this field, the specific meanings of the above terms in this patent can be understood according to specific circumstances.

[0061] like Figure 1-3 As shown in the figure, the smart distribution grid peak load market transaction optimization method taking into account flexibility evaluation includes the following steps:

[0062] Step 1: Build an operational model for flexible loads

[0063] Collect detailed topological information of the smart distribution network, including the connection relationship between each node (such as Figure 3 as shown), line parameters, and the type and location of access equipment, etc. Figure 2The schematic diagram shows two different structures of smart distribution networks providing services to the upper distribution network. The smart distribution network is mainly composed of renewable energy, flexible load resources, control center, aggregator, smart meter and other elements. Renewable energy as power generation resources meets the internal power demand of the smart distribution network, including photovoltaic and wind power generation. Flexible load resources are used to solve the absorption and shortage problems caused by the random output of renewable energy on the one hand, and on the other hand, they are connected to aggregators to form large-scale resources. The flexible loads of the smart distribution network include the energy storage system, electric vehicles, and temperature control loads shown in the figure. After meeting the internal absorption, these flexible loads participate in the market externally; the aggregator then reports the peak load to the upper power grid through the dispatch center to participate in the peak load market. The control center, as the intermediate link between the aggregator and the upper power grid, first receives the peak load demand signal of the upper power grid and sends it to the aggregator, then receives the peak load reported by the aggregator, and finally the dispatch center completes the clearing according to the current price, supplies the power in the smart distribution network to the upper power grid, and completes the peak load filling of the upper power grid. Smart meters are used as devices to monitor the operating status of flexible load resources in the smart distribution network.

[0064] S11: Construction of energy storage system operation model

[0065] The energy storage system is different from traditional power generation and consumption equipment and has charging and discharging characteristics. When the power system needs to adjust flexibility upward, the energy storage system discharges or reduces charging power to meet the upward fluctuation of the net load; when the power system needs to adjust flexibility downward, the energy storage system charges or reduces discharge power to meet the downward fluctuation of the net load. Therefore, the operation model of the energy storage system is as shown in equations (1)-(5):

[0066]

[0067]

[0068] In the formula is the discharge power of the energy storage system at time t; is the charging power of the energy storage system at time t; are two binary integer variables used to represent the charge and discharge status; when When it is 1, it means that the energy storage system is in the discharge state; when When it is 1, it indicates that the energy storage system is in the charging state; Formula (9) ensures that the energy storage system cannot be in the charging and discharging state at the same time. is the capacity of the energy storage system at time t; represents the capacity of the energy storage system in the previous period; τ represents the time interval; k c , k d are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively.

[0069] S12: Construction of temperature control load operation model

[0070] Temperature control loads, such as air conditioners and refrigerators, are different from energy storage systems. They are flexible load resources with one-way energy exchange and are widely considered to have great potential to support the operation of power systems. Temperature control loads are usually adjusted within the range allowed by load users. Without affecting the comfort of users, temperature control loads are called to provide flexibility. The temperature control load operation model is shown in equations (6)-(9):

[0071]

[0072] In the formula are the indoor temperature and ambient temperature of the temperature control load at time t respectively; is the thermal resistance and thermal capacity of the temperature control load; η W is the cooling / heating coefficient of the temperature control load; P t W is the power of the temperature control load at time t; is the maximum power of the temperature control load; The temperature set by the user; Δθ W The tolerable temperature fluctuation value set by the user.

[0073] S13: Construction of electric vehicle operation model

[0074] In terms of energy storage, electric vehicles and energy storage devices are similar, but electric vehicles are a means of transportation, and their access and off-grid times are closely related to the travel needs of the owner. At the same time, the battery capacity of electric vehicles is also constrained by the subjective behavior of the owner, and the electric vehicle needs to be charged and discharged under the premise of satisfying the owner's travel needs.

[0075] To simplify the problem, this embodiment assumes that the owner sets the scheduled departure time and the expected off-grid power before charging, and the owner does not intervene in the electric vehicle during the charging and discharging process. The electric vehicle operation model is shown in equations (10)-(15).

[0076]

[0077] In the formula is the charging and discharging power of the i-th electric vehicle in the t-th period; is the upper limit of charging and discharging power of the i-th electric vehicle; is a binary integer variable. When it is 1, it means the electric vehicle is in charging state. When it is 1, it means that the electric vehicle is in the discharging state; is the battery capacity of the i-th electric vehicle at time t; They are the time when electric vehicles enter and leave the grid; are the charging and discharging efficiency coefficients of electric vehicles respectively; and They are the minimum and maximum capacity of electric vehicle batteries, the off-grid capacity required for car owners to travel, and the expected off-grid capacity set by users.

[0078] For the energy storage system, the following parameter data are obtained: capacity: 1.21MWh, charging efficiency: 80%, discharging efficiency: 75%, charging power limit: 0.375MW, discharging power limit: 0.375MW.

[0079] For temperature control loads, record information such as thermal resistance, thermal capacity, cooling / heating coefficient, user-set temperature range (20-25°C), and tolerable temperature fluctuation value (±5°C).

[0080] For electric vehicles, the following parameter data are obtained: battery capacity: 1MWh, charging power limit: 0.375MW, discharging power limit: 0.375MW, grid access time: 14:00, grid-off time: 17:00, expected grid-off power: 85%. At the same time, the peak-to-valley difference historical data of the upper-level distribution network is collected to calculate the peak-to-valley difference unit penalty cost.

[0081] Step 2: Establish a distribution network flexibility assessment model

[0082] The objective function is set to minimize the overall internal operating cost of the smart distribution network. Its operating cost also takes into account the benefits of the smart distribution network participating in the peak-shaving market. The benefit is initially set to 0. The operating cost objective function taking into account the market benefits is shown in formula (16):

[0083]

[0084] In the formula The operating cost of the energy storage system; is the operating cost of the temperature control load; is the operating cost of electric vehicles; C loss is the network loss cost, c loss is the unit average operating cost of generators and flexible load resources, P loss is the active power loss of the smart distribution network, and the two together constitute the network loss cost; c E 、c W 、c V are the unit operating costs of the energy storage system, temperature control load, and electric vehicles to maintain the internal operation of the smart grid; C profit The income obtained by participating in the peak-shaving market comes from solving the peak-shaving market transaction optimization model. During initial operation, this parameter is set to 0.

[0085] On the basis of the flexible load operation constraints, power balance constraints are added to ensure the local consumption of wind and solar power and actively meet the grid operation requirements, as shown in formula (17).

[0086]

[0087] Where P g P is the output sequence of the generator in the smart distribution network; PV , P W They are the photovoltaic and wind turbine output sequences respectively; Charge and discharge power sequence for energy storage system; is the charging and discharging power sequence of the i-th electric vehicle; P w is the temperature control load power sequence; P load Other load sequences for smart distribution network.

[0088] Step 3: Run the flexibility assessment model

[0089] S31: The operating constraints of flexible load resources include energy storage system constraints, temperature control load constraints, and electric vehicle charging and discharging constraints. For the energy storage system, according to its energy storage system operation model, a binary integer variable is used to represent the charging and discharging state, and the optimal charging and discharging strategy is found under the conditions that charging and discharging cannot be performed simultaneously, as well as the upper and lower limits of capacity and the charging and discharging efficiency constraints. For the temperature control load, according to its temperature control load operation model, the flexibility requirements are met by adjusting the power within the temperature set by the user and the tolerable temperature fluctuation range. For electric vehicles, combined with the conditions such as its grid connection and off-grid time and the expected off-grid power set by the user, the charging and discharging state is controlled by a binary integer variable, and flexibility supply is provided under the premise of meeting the travel needs of the car owner. Table 1 shows the set constraints.

[0090] Table 1 Constraints and simulation parameters

[0091]

[0092] S32: The objective function of the distribution network flexibility evaluation model under the constraints set by step S31 is simulated by solving the optimization problem. If the tolerance temperature obtained last time is 18°C, which does not meet the preset tolerable temperature fluctuation range of 20-30°C for the temperature control load, return to S31 and readjust; until the optimal solution of all the constraints set by S31 is met, the external flexibility supply index values ​​of various types of flexible load resources are calculated as the constraints of the intelligent distribution network peak-shaving market transaction optimization model and the results are passed to step 4.

[0093] Based on the internal optimization scheduling results of the smart distribution network, under the premise of meeting local consumption, the energy storage system provides flexibility by changing the charging and discharging power. After the smart distribution network itself is running, the change in charging and discharging power is used as the external flexibility supply of the energy storage system. The model is shown in Equation (18) and Equation (19).

[0094]

[0095] In the formula They are the upward and downward flexibility supplies provided by the energy storage system at time t respectively.

[0096] When calculating the upward and downward flexibility supply index values ​​of the temperature control load, the temperature change range set by the user needs to be considered. The models are shown in equations (20) and (21).

[0097]

[0098] In the formula, Provides an index value for the upward and downward flexibility provided by the temperature control load at time t.

[0099] Electric vehicles are similar to energy storage systems, which provide flexibility by changing the charging and discharging power. However, while considering the time when electric vehicles are connected to the grid / off the grid, they also need to meet the travel needs of electric vehicle users. The electric vehicle flexibility supply model is shown in Equations (22) and (23).

[0100]

[0101] In the formula is the upward and downward flexibility supply of the ith electric vehicle in the tth period; t = lea represents the off-grid time. When the electric vehicle is off-grid, the off-grid capacity set by the electric vehicle user should be considered when calculating the upward flexibility supply index value.

[0102] After the smart distribution network itself is running smoothly, the output change that can still be provided by flexible load resources such as energy storage systems, temperature control loads, and electric vehicles in the smart distribution network constitutes the upper and lower limits of the flexibility that the smart distribution network can participate in the peak-shaving market. The flexibility supply model of the smart distribution network is shown in formula (24).

[0103]

[0104] In the formula Provide upward and downward flexibility for the smart distribution network to the external distribution network.

[0105] Step 4: Establish a smart distribution network peak load market transaction optimization model and optimize

[0106] S41: Establish a transaction optimization model for the peak-shaving market of the smart distribution network, wherein the model is constrained by the upward and downward flexibility supply index values ​​of the smart distribution network in step S32, and adds satisfaction benefits, peak-valley difference penalties, etc. to the objective function of maximizing the peak-shaving market benefits to ensure user satisfaction and the peak-shaving effect of the external power grid. By establishing the objective function and constraints, the transaction optimization model for the peak-shaving market of the smart distribution network is established.

[0107] S42: Optimize the calculation with the goal of maximizing the peak-shaving market revenue while taking into account the satisfaction revenue and the peak-valley difference penalty. The revenue includes user satisfaction and peak-shaving effect cost to ensure user satisfaction and peak-shaving effect. The objective function of the market transaction optimization model is shown as follows:

[0108]

[0109] Among them C s is the satisfaction benefit brought by the temperature control load and the electric vehicle. The specific calculation method is shown in the following formula (26) and formula (27); C profit Generate peak load revenue for smart distribution grid; To participate in the peak load response; and is the cost of energy storage system, temperature control load, and electric vehicles participating in the peak load regulation of the upper distribution network; C peak-vally is the peak-to-valley difference penalty cost caused by the peak-to-valley difference; L peak-vally is the peak-to-valley difference of the upper distribution network; α, β, δ and c peak-vally are respectively the user satisfaction coefficient of electric vehicle, temperature control load, peak-shaving profit coefficient of intelligent distribution network and unit penalty cost of peak-valley difference; since the cost is affected by many factors, the present invention calculates the peak-shaving cost C of the upper distribution network period by statistical analysis total The total peak-to-valley difference L total-peak The parameter is determined by the ratio of and They are the satisfaction of electric vehicle and temperature control load users respectively.

[0110] Assuming that the energy storage system belongs to the distribution network operator and does not need to meet its satisfaction, the specific calculation methods for the user satisfaction of electric vehicles and temperature control loads are shown in Equations (26) and (27).

[0111]

[0112] Where △P t W is the response of temperature control load participating in the peak load regulation market; μ, ν and λ are satisfaction coefficients.

[0113]

[0114] in The battery capacity when the user finishes charging is closer to the off-grid capacity set by the user after participating in electricity market transactions, the greater the user satisfaction.

[0115] S43: Constraining the market transaction optimization model to obtain the market transaction structure

[0116] The constraints received by the market transaction optimization model are the flexibility supply index values ​​of the flexibility evaluation model obtained in step 3, as shown in formulas (28)-(30):

[0117]

[0118] in It provides index values ​​for the upward and downward flexibility of the smart distribution network to the external distribution network. The flexibility supply index values ​​will be obtained by the flexibility evaluation model. and Both are Boolean variables, which are used to ensure that the smart distribution network cannot participate in peak shaving and valley filling at the same time.

[0119] Step 5: The market transaction optimization model performs transaction strategy optimization iteration according to the result of step 4, and repeats steps 2 to 4; wherein, the peak-shaving market revenue C profit Feedback to the distribution network flexibility evaluation model in step 2 to update its objective function, add power balance constraints on the basis of the flexible load operation constraints in step 3, calculate the upward and downward flexibility supply values ​​that the smart distribution network can participate in the peak-shaving market under the premise of satisfying local consumption, re-evaluate the flexibility of the smart distribution network, and complete an iteration;

[0120] After multiple rounds of iterations, when the difference between the results of two iterations is less than 0.1 yuan, the iteration ends and the solution is completed. Among them, the optimization variables are the charging and discharging power of the temperature control load, electric vehicles, and energy storage systems in each period. The change of charging and discharging power will affect the flexibility supply value.

[0121] The obtained flexibility of temperature-controlled loads, electric vehicles, and energy storage systems in different time periods is then output in the form of a chart for peak load regulation in the upper distribution network.

[0122] Specifically, Figure 2 The smart distribution network structure on the lower left side, the control center receives the peak-shaving demand signal from the upper-level distribution network, obtains the flexibility supply value for each time period based on the smart distribution network peak-shaving market transaction optimization method taking into account flexibility evaluation, updates the peak-shaving demand of the upper-level distribution network, and then sends it to the aggregator, and then accepts the peak-shaving amount declared by the aggregator. Finally, the dispatch center completes the clearing according to the current price.

[0123] like Figure 2The smart distribution network structure on the lower right side uploads the flexibility supply value for each time period obtained based on the smart distribution network peak-shaving market transaction optimization method taking flexibility evaluation into account to the upper-level distribution network for peak-shaving use.

[0124] like Figure 4-5 As shown, the flexibility evaluation results of distributed smart distribution networks with and without considering peak-shaving market revenue are shown respectively. It can be clearly seen that whether considering market revenue can affect the effect of flexibility evaluation. Figure 6 A comparison chart is shown with and without considering flexibility assessment at different times, from which it can be clearly seen that whether flexibility assessment is considered will affect the electricity market participation amount. Figure 7 A comparison chart showing the peak load regulation results of the upper distribution network with and without considering flexibility evaluation is shown.

[0125] Although the preferred embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments, which are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which fall within the scope of protection of the present invention.

Claims

1. A method for optimizing transactions in a peak-shaving market of a smart distribution network taking flexibility evaluation into account, comprising: Step 1: Establish an operation model for the flexible load of the smart distribution network, including building an energy storage system operation model, a temperature control load operation model, and an electric vehicle operation model; Step 2: Establish a flexibility evaluation model for the smart distribution network; the model takes the lowest overall internal operating cost of the smart distribution network as the objective function, and takes the flexible load resource operation constraints and power balance constraints as the constraints, where the flexible load resource operation constraints include energy storage system constraints, temperature control load constraints, and electric vehicle charging and discharging constraints; Step 3: Based on the established flexible load resource operation constraints and power balance constraints, run the flexibility evaluation model to obtain the upward and downward flexibility supply index values ​​of various flexible load resources; Step 4: Establish a transaction optimization model for the peak-shaving market of the smart distribution network, with the peak-shaving market revenue maximization as the objective function while taking into account the satisfaction revenue and the peak-valley difference penalty, and the upward and downward flexibility supply index values ​​obtained in step 3 as constraints; Step 5: Feed the peak-shaving market revenue obtained in step 4 back to step 2 to update the objective function of the smart distribution network flexibility evaluation model; Repeat steps 2 to 4 to iterate until the difference between the results of the current and previous iterations is less than the preset value, then end the iteration and output the flexibility supply values ​​of the temperature-controlled load, electric vehicle, and energy storage system in each time period for peak load regulation of the upper distribution network.

2. The method for optimizing transactions in the peak load market of a smart distribution network taking flexibility evaluation into account according to claim 1 is characterized in that: The step 2 specifically includes: The objective function is to minimize the overall internal operating cost of the smart distribution network, where the operating cost takes into account the benefits obtained by the smart distribution network participating in the peak-shaving market. The operating cost objective function taking into account the market benefits is shown as follows: In the formula The operating cost of the energy storage system; The operating cost of the temperature control load; is the operating cost of electric vehicles; C loss is the network loss cost, c loss is the unit average operating cost of generators and flexible load resources, P loss is the active power loss of the smart distribution network, and the two together constitute the network loss cost; c E 、c W 、c V are the unit operating costs of the energy storage system, temperature control load, and electric vehicles to maintain the internal operation of the smart grid; C profit The income obtained from participating in the peak-shaving market comes from solving the peak-shaving market transaction optimization model. During initial operation, this parameter is set to 0. Power balance constraints are added on the basis of flexible load operation constraints, including: Where P g P is the output sequence of the generator in the smart distribution network; PV , P W They are photovoltaic and wind turbine output sequences respectively; Charge and discharge power sequence for energy storage system; is the charging and discharging power sequence of the i-th electric vehicle; P w is the temperature control load power sequence; P load Other load sequences for smart distribution network.

3. The method for optimizing transactions in the peak load market of a smart distribution network taking flexibility evaluation into account according to claim 1 is characterized in that: Step three specifically includes: S31: Establish energy storage system constraints, temperature control load constraints, and electric vehicle charging and discharging constraints; during initial operation, based on the operation model of the flexible load established in step 1, determine the energy storage system charging and discharging power, the temperature control load power, and the electric vehicle charging and discharging power respectively; S32: Use the optimization solution method to simulate the objective function of the distribution network flexibility assessment model under the constraints set by S31. If any parameter does not meet the constraints set by S31, return to step S31 and readjust until the optimal solution that meets all the constraints set in step S31 is found, and the upward and downward flexibility supply index values ​​of various types of flexible load resources are obtained.

4. The method for optimizing transactions in the peak load market of a smart distribution network taking flexibility evaluation into account according to claim 1 is characterized in that: Step 4 specifically includes: S41: Establishing a smart distribution network peak-shaving market transaction optimization model with the peak-shaving market revenue maximization and taking into account the satisfaction revenue and the peak-valley difference penalty as the objective function, the objective function is as follows; Among them C s is the satisfaction benefit brought by the temperature control load and the electric vehicle. The specific calculation method is shown in the following formula (26) and formula (27); C profit Generate peak load revenue for smart distribution grid; To participate in the peak load response; and is the cost of energy storage system, temperature control load, and electric vehicles participating in the peak load regulation of the upper distribution network; C peak-vally is the peak-to-valley difference penalty cost caused by the peak-to-valley difference; L peak-vally is the peak-to-valley difference of the upper distribution network; α, β, δ and c peak-vally are respectively the user satisfaction coefficient of electric vehicle, temperature control load, peak-shaving profit coefficient of intelligent distribution network and unit penalty cost of peak-valley difference; since the cost is affected by many factors, the present invention calculates the peak-shaving cost C of the upper distribution network period by statistical analysis total The total peak-to-valley difference L total-peak The parameter is determined by the ratio of and They are the satisfaction of electric vehicle and temperature control load users respectively; S42: Taking the upward and downward flexibility supply index values ​​of the smart distribution network in step 3 as constraints, the market transaction structure is obtained, as shown below: in Provide index values ​​for the upward and downward flexibility of the smart distribution network to the external distribution network. The flexibility supply index values ​​will be obtained by the flexibility evaluation model; and Both are Boolean variables, which are used to ensure that the smart distribution network cannot participate in peak shaving and valley filling at the same time.

5. The method for optimizing transactions in the peak load-shaving market of a smart distribution network taking flexibility evaluation into account according to claim 1, characterized in that: In the iteration process, the energy storage system charging and discharging power, the temperature control load power and the electric vehicle charging and discharging power in step S31 are iteratively updated according to the peak load market revenue obtained in the previous calculation process; Under the premise of satisfying local consumption, the energy storage system provides upward and downward flexibility by changing the charging and discharging power. The energy storage system flexibility supply model is shown in the following formula: In the formula They are the upward and downward flexibility supplies provided by the energy storage system at time t respectively; The temperature control load is provided with upward and downward flexibility by changing the temperature change range set by the user. The temperature control load flexibility supply model is shown in the following formula: In the formula, Provide index values ​​for the upward and downward flexibility provided by the temperature control load at time t; Electric vehicles provide flexibility by changing the charging and discharging power, while taking into account the time when electric vehicles are connected to the grid / off the grid and the need to meet the travel needs of electric vehicle users. The electric vehicle flexibility supply model is shown in the following formula: In the formula is the upward and downward flexibility supply of the ith electric vehicle in the tth period; t = lea represents the off-grid time; After the smart distribution network itself is running, the output change that can still be provided by the flexible load resources such as energy storage systems, temperature control loads, and electric vehicles in the smart distribution network constitutes the upper and lower limits of the flexibility of the smart distribution network to participate in the peak-shaving market. The flexibility supply model of the smart distribution network is shown in the following formula: In the formula Provide upward and downward flexibility for the smart distribution network to the external distribution network.

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