A Secondary Frequency Regulation Method for Virtual Power Plants Considering Integrated Energy Resources

Abstract

The present invention discloses a secondary frequency regulation method for a virtual power plant considering comprehensive energy resources, including: constructing a market mechanism for the virtual power plant to participate in secondary frequency regulation; constructing an optimization bidding model for the comprehensive energy virtual power plant to participate in secondary frequency regulation under the day-ahead market mechanism, solving the objective function of the optimization bidding model to obtain the bidding frequency regulation capacity, capacity price and mileage price of the comprehensive energy virtual power plant, as well as the frequency regulation capacity of each aggregation entity of the comprehensive energy virtual power plant; constructing an optimization model for the comprehensive energy virtual power plant to participate in secondary frequency regulation under the intraday market mechanism, solving the objective function of the optimization model to obtain the frequency regulation mileage of each aggregation entity of the comprehensive energy virtual power plant and the final frequency regulation revenue obtained by the comprehensive energy virtual power plant. The present invention constructs a secondary frequency regulation market mechanism, and the comprehensive energy virtual power plant participates in the two-stage rolling bidding decision of day-ahead and intraday for secondary frequency regulation. Furthermore, the mutual substitution ability evaluation of comprehensive energy resources can be carried out.

Description

Technical Field

[0001] The present invention relates to a comprehensive energy virtual power plant system, and particularly to a secondary frequency regulation method for a virtual power plant considering comprehensive energy resources. Background Art

[0002] It is necessary to build a new power system with new energy as the main body. On the one hand, with the large-scale grid connection of new energy power generation, the power system shows new characteristics of continuously increasing new energy penetration rate, reduced system inertia, and severe challenges to frequency security. On the other hand, the number of traditional units available for participating in frequency regulation decreases, and the traditional frequency regulation method of increasing the reserve capacity of generators on the power generation side cannot quickly respond on a large scale. The integrated energy system can achieve multi-energy coordination to increase the flexibility of system frequency regulation. Response resources such as electric-thermal energy storage devices, flexible loads, and alternative loads in the integrated energy system can be used to solve the frequency problems of the power system. In recent years, ancillary service markets in the United States, Europe, etc. have been opened to third-party participants and non-traditional energy sources [1] , such as battery energy storage and industrial demand response. However, due to the decentralized layout and independent control of these flexibility resources, they cannot be directly connected to the transmission grid, and it is impossible to overall plan the flexibility resources within the region to obtain the economically optimal frequency regulation scheme. The virtual power plant realizes the integration and complementarity of multiple resources through advanced communication, metering, control and other rich regulation means, and integrates and uniformly schedules multiple entities [2] , is not restricted by geography, can be flexibly scheduled according to operating conditions, and has superiority in solving frequency regulation problems.

[0003] At the same time, in the context of multi-energy complementarity of integrated energy, the virtual power plant has the characteristics of multiple stakeholder interests and high flexibility, and its market participation ability has been greatly improved. Literature [3] shows that the virtual power plant can affect the changes in wholesale electricity market prices. Literature [4] aggregates commercial demand response through the virtual power plant to participate in the day-ahead market. Literature [5] shows that the virtual power plant can reduce the costs brought by the uncertainties of resources such as demand-side response and renewable energy by utilizing the complementary characteristics between different resources. Literature [6] established a two-stage stochastic model to optimize the bidding of the virtual power plant in the energy market and the reserve market. However, these literatures mostly focus on the optimal scheduling of the virtual power plant's participation in the day-ahead energy market, and do not conduct a complete modeling of the bidding process of the virtual power plant's participation in the frequency regulation ancillary service market, nor do they study the impact of the optimal scheduling results of the virtual power plant under the conditions of the frequency regulation ancillary service market on the power system frequency.

[0004] [References]

[0005] [1] N. Good and P. Mancarella, “Flexibility in Multi-Energy Communities with Electrical and Thermal Storage: A Stochastic, Robust Approach for Multi-Service Demand Response,” IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 503-513, Jan, 2019.

[0006] [2] Tian Liting, Cheng Lin, Guo Jianbo, Wang Xuanyuan, Yun Qiuchen, Gao Wenzhong. Research Review on the Management and Interaction Mechanism of Distributed Energy by Virtual Power Plant [J]. Power System Technology, 2020, 44(06): 2097-2108.

[0007] [3] Y. Cao, C. Li, X. Liu, “Optimal Scheduling of Virtual Power Plant with Battery Degradation Cost,” IET Generation Transmission & Distribution, vol. 10, no. 3, Apr, 2016.

[0008] [4] A. Wss, B. Eg, A. Es, “Techno-Economic Assessment of Consumers' Participation in the Demand Response Program for Optimal Day-Ahead Scheduling of Virtual Power Plants-Science Direct,” Alexandria Engineering Journal, vol. 59, no. 1, pp. 399-415, Apr, 2020.

[0009] [5]M.M. Othman, Y.G. Hegazy, A.Y. Abdelaziz, “Electrical Energy Management in Unbalanced Distribution Networks Using Virtual Power Plant Concept,” Electric Power Systems Research, vol. 145, pp. 157 - 165, Apr, 2017.

[0010] [6]N. Pourghaderi, M. Fotuhi - Firuzabad, M. Moeini - Aghtaie, “Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant,” IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 5100 - 5111, Oct, 2018. Summary of the Invention

[0011] In view of the above - mentioned prior art, the present invention proposes a secondary frequency regulation method for a virtual power plant considering integrated energy resources, constructs a secondary frequency regulation market mechanism, and the integrated energy virtual power plant participates in the day - ahead and intra - day two - stage rolling bidding decision - making for secondary frequency regulation. Furthermore, the mutual substitution ability evaluation of integrated energy resources can be carried out.

[0012] To solve the above - mentioned technical problems, the secondary frequency regulation method for the virtual power plant of the present invention mainly includes:

[0013] 1) Construct a market mechanism for the virtual power plant to participate in secondary frequency regulation, including a day - ahead market model and an intra - day market model;

[0014] The intra - day market model is as follows: in the intra - day market, each bidding entity optimizes and clears according to the winning bid results in the day - ahead market with the goal of minimizing the real - time frequency regulation cost on the operating day. Each winning bid entity outputs power according to the clearing results, and the combined revenue of the day - ahead and intra - day markets is settled at the end of the operating day.

[0015] 2) Construct an optimal bidding model for the integrated energy virtual power plant to participate in secondary frequency regulation under the day - ahead market mechanism, solve the objective function of the optimal bidding model, and obtain the bidding frequency regulation capacity, the bidding frequency regulation capacity price, the bidding frequency regulation mileage price of the integrated energy virtual power plant, and the frequency regulation capacities of the integrated energy virtual power plant aggregating battery energy storage entities, aggregating thermal energy storage equipment entities, aggregating flexible load entities, and aggregating alternative load entities.

[0016] 3) Construct an optimization model for the integrated energy virtual power plant to participate in secondary frequency regulation under the intraday market mechanism, solve the objective function of the optimization model, obtain the frequency regulation mileage of the integrated energy virtual power plant aggregating battery energy storage entities, aggregating heat storage equipment entities, aggregating flexible load entities, and aggregating alternative load entities, and the final frequency regulation revenue obtained by the integrated energy virtual power plant. At the same time, the integrated energy virtual power plant considers the time-delay characteristics of the aggregated resources to conduct real-time simulation and evaluate the frequency regulation effect;

[0017] 4) Use the frequency regulation mileage of the integrated energy virtual power plant aggregating battery energy storage entities, aggregating heat storage equipment entities, aggregating flexible load entities, and aggregating alternative load entities obtained in step 3) as the actual output of the virtual power plant participating in the secondary frequency regulation market.

[0018] Furthermore, in step 1) of the method of the present invention, the day-ahead market model is as follows: in the day-ahead market, the operation center announces the frequency regulation capacity and frequency regulation performance requirements. Each bidding entity participates in the day-ahead market bidding with the capacity price, mileage price, and frequency regulation capacity as the bidding targets based on its own optimal operation strategy; the operation center clears with the goal of minimizing the capacity cost to meet the secondary frequency regulation demand, and announces the winning capacity, capacity price, and mileage price of each bidding entity; the intraday market model is as follows: in the intraday market, each bidding entity optimizes and clears according to the winning results of the day-ahead market with the goal of minimizing the real-time frequency regulation cost on the operating day. Each winning entity outputs according to the clearing results and conducts a combined revenue settlement for the day-ahead and intraday markets at the end of the operating day.

[0019] The specific steps of step 2) of the method of the present invention include:

[0020] 2-1) Determine the revenue of the virtual power plant participating in the day-ahead secondary frequency regulation market bid:

[0021]

[0022] Where: N T is the total number of dispatching moments; t is the frequency regulation moment, and its value is between 1 and N T ; are the bid capacity price and mileage price of the integrated energy virtual power plant respectively; are the frequency regulation capacity and frequency regulation mileage of the integrated energy virtual power plant respectively;

[0023] 2-2) Under the secondary frequency regulation market mechanism, construct a virtual power plant model based on the integrated energy system to meet the secondary frequency regulation market demand;

[0024]

[0025] Where: N Tis the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 and N T ; N i is the total number of battery energy storages, and i is the battery energy storage serial number; N n is the total number of flexible loads, and n is the flexible load serial number; N j is the total number of replaceable loads, and j is the replaceable load serial number; is the total number of heat storage devices, is the heat storage device serial number; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of signals of the fast frequency regulation signal within the scheduling period, and its value is between 1 and N D ; N A is the total number of traditional frequency regulation signals within the scheduling period; A is the number of signals of the traditional frequency regulation signal within the scheduling period, and its value is between 1 and N A ; are respectively the unit frequency regulation capacity costs of the integrated energy virtual power plant aggregating battery energy storages, flexible loads, replaceable loads, and heat storage devices; are respectively the unit frequency regulation mileage costs of the integrated energy virtual power plant aggregating battery energy storages, flexible loads, replaceable loads, and heat storage devices; are respectively the frequency regulation capacities of battery energy storages, flexible loads, replaceable loads, and heat storage devices; are respectively the frequency regulation mileages of battery energy storages, flexible loads, replaceable loads, and heat storage devices;

[0026] 2-3) According to the preliminary evaluation of the frequency regulation effect based on the regulation demand information announced in the day-ahead secondary frequency regulation market, as follows

[0027]

[0028] Among them: are respectively the frequency regulation mileages of the integrated energy virtual power plant responding to fast frequency regulation signals and traditional frequency regulation signals, are respectively the frequency regulation capacities of the integrated energy virtual power plant responding to fast frequency regulation signals and traditional frequency regulation signals; R egD , R egA are respectively the coefficients of fast frequency regulation signals and traditional frequency regulation signals in the intraday market; ω D , ω A is the frequency regulation error penalty coefficient;

[0029] 2-4) The objective function of the day-ahead optimization bidding model of the integrated energy virtual power plant is as follows:

[0030] maxF 1 = ω 1 f 1 - ω 2 f 2 - ω3 f 3

[0031] Among them: ω 1 , ω 2 , ω 3 are respectively the function penalty coefficients,

[0032] 2 - 5) Solve the above objective function to obtain the bidding frequency regulation capacity of the integrated energy virtual power plant Bidding frequency regulation capacity price Bidding frequency regulation mileage price and the frequency regulation capacities of the aggregated battery energy storage body, aggregated thermal energy storage equipment body, aggregated flexible load body, and aggregated alternative load body of the integrated energy virtual power plant

[0033] The specific steps of step 3) in the method of the present invention include:

[0034] 3 - 1) Determine the revenue obtained by the integrated energy virtual power plant in the secondary frequency regulation day - ahead market:

[0035]

[0036] Among them: N T is the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 - N T ; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of fast frequency regulation signals within the scheduling period, and its value is between 1 - N D ; N A is the total number of traditional frequency regulation signals within the scheduling period; A is the number of traditional frequency regulation signals within the scheduling period, and its value is between 1 - N A ; R egD , R egA are respectively the fast frequency regulation signal and traditional frequency regulation signal coefficients in the day - ahead market; are respectively the bidding capacity price and mileage price of the integrated energy virtual power plant; is the nodal marginal price of the location where the virtual power plant is located; is the capacity won in the day - ahead market by the virtual power plant, and its value is obtained after day - ahead clearing according to the bidding frequency regulation capacity of the virtual power plant in step 2) ; are respectively the won capacity for calculation adjusted according to the traditional frequency regulation signal, and the won capacity for calculation adjusted according to the fast frequency regulation signal; are respectively the mileage of the integrated energy virtual power plant participating in frequency regulation, the frequency regulation mileage of responding to the fast frequency regulation signal, and the frequency regulation mileage of participating in the traditional frequency regulation signal;

[0037] 3-2) The allocation of the frequency regulation mileage for each aggregation entity is as follows:

[0038]

[0039] Where: N i is the total number of battery energy storages, and i is the battery energy storage serial number; N n is the total number of flexible loads, and n is the flexible load serial number; N j is the total number of replaceable loads, and j is the replaceable load serial number; is the total number of heat storage devices, is the heat storage device serial number; are the unit capacity costs of the aggregated battery energy storage, flexible load, replaceable load, and heat storage device of the integrated energy virtual power plant respectively; are the unit mileage costs of the aggregated battery energy storage, flexible load, replaceable load, and heat storage device of the integrated energy virtual power plant respectively; are the bid frequency regulation capacity and bid frequency regulation mileage of the integrated energy virtual power plant respectively; are the frequency regulation capacities of the battery energy storage, flexible load, replaceable load, and heat storage device respectively; are the frequency regulation mileages of the battery energy storage, flexible load, replaceable load, and heat storage device respectively;

[0040] 3-3) The objective function of the optimization model for the integrated energy virtual power plant to participate in the secondary frequency regulation intraday market is as follows:

[0041] maxF 2 = ω 4 f 4 - ω 5 f 5

[0042] Where: ω 4 , ω 5 are the function penalty coefficients respectively;

[0043] 3-4) Solve the objective function of the above optimization model to obtain the frequency regulation mileages of the aggregated battery energy storage entity, aggregated heat storage device entity, aggregated flexible load entity, and aggregated replaceable load entity of the integrated energy virtual power plant and the frequency regulation revenue f 4 finally obtained by the integrated energy virtual power plant.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] The integrated energy virtual power plant proposed by the present invention aggregates a variety of distributed electric and thermal resources including battery energy storage, thermal energy storage equipment, flexible loads, and alternative loads, makes full use of the complementary characteristics among the electric and thermal resources, and improves the resource utilization efficiency. In addition, the integrated energy virtual power plant optimization bidding method proposed by the present invention fully models the bidding process, provides an effective solution for the virtual power plant to aggregate various resources of the integrated energy system to participate in secondary frequency modulation, and through simulation calculations based on the improved IEEE 33-node distribution and 45-node heat network coupling system example, obtains the optimal strategy and benefits of the integrated energy virtual power plant participating in the secondary frequency modulation market, proving the effectiveness of the present invention. Description of the Drawings

[0046] Figure 1 It is a frequency modulation market signal diagram, where (a) is the fast frequency modulation signal diagram of the frequency modulation market, and (b) is the traditional frequency modulation signal diagram of the frequency modulation market;

[0047] Figure 2 It is a frequency modulation market mileage factor diagram;

[0048] Figure 3 It is the structure diagram of the thermal energy storage equipment described in the present invention;

[0049] Figure 4 It is the block diagram of the integrated energy virtual power plant participating in the secondary frequency modulation market model;

[0050] Figure 5 It is the example network topology diagram described in the present invention;

[0051] Figure 6 It is the winning bid capacity diagram of the virtual power plant participating in the frequency modulation market bidding in the example of the present invention;

[0052] Figure 7 It is the frequency modulation capacity diagram of each aggregation entity of the virtual power plant in the example of the present invention;

[0053] Figure 8 It is the frequency modulation effect diagram of the virtual power plant in the example of the present invention;

[0054] Figure 9 It is the energy state diagram of the battery energy storage and the thermal energy storage device in the example of the present invention, where (a) is the energy state diagram of the thermal energy storage equipment in the original example, (b) is the energy state diagram of the battery energy storage in the original example, and (c) is the energy state diagram of the battery energy storage in the comparative example;

[0055] Figure 10 It is the comparison diagram of the battery energy storage life and the virtual power plant benefits under different scenarios in the example of the present invention, where (a) is the battery energy storage life under different scenarios, and (b) is the revenue situation of each aggregation entity participating in the frequency modulation market under different scenarios. Detailed Embodiment

[0056] The following further describes the present invention in conjunction with the accompanying drawings and specific embodiments, but the following embodiments are by no means any limitation to the present invention.

[0057] The design idea of a secondary frequency regulation method for a virtual power plant considering integrated energy resources proposed by the present invention is as follows:

[0058] 1) Construct a market mechanism for the virtual power plant to participate in secondary frequency regulation, mainly including a day-ahead market and an intraday market model. The day-ahead market model is as follows: in the day-ahead market, the operation center announces the frequency regulation capacity and frequency regulation performance requirements. Each bidding entity participates in the day-ahead market bidding with the capacity price, mileage price, and frequency regulation capacity as the bidding targets based on its own optimal operation strategy. The operation center clears with the goal of minimizing the capacity cost to meet the secondary frequency regulation demand capacity, and announces the winning capacity, capacity price, and mileage price of each bidding entity. The intraday market model is as follows: in the intraday market, each bidding entity optimally clears according to the winning results in the day-ahead market with the goal of minimizing the real-time frequency regulation cost on the operating day. Each winning entity generates power according to the clearing results and conducts a combined revenue settlement for the day-ahead and intraday markets at the end of the operating day. The frequency regulation signals and mileage factors announced in the frequency regulation market are as shown in Figure 1 and Figure 2 respectively;

[0059] 2) Construct an optimal bidding model for the integrated energy virtual power plant to participate in secondary frequency regulation under the day-ahead market mechanism. First, model the integrated energy system virtual power plant model. The distributed resource models of each aggregation entity include a flexible load model, an alternative load model, a heat pump model, and an electric thermal energy storage device model. Further, based on the determined secondary frequency regulation intraday market mechanism, establish an integrated energy virtual power plant optimal bidding model, and optimize with the maximum revenue and optimal frequency regulation effect of the integrated energy virtual power plant as the objective function. In addition to meeting the self-constraints of devices such as electric thermal energy storage devices, flexible loads, alternative loads, and heat pumps, the optimization constraint conditions also need to meet the virtual power plant aggregation condition constraints. Solve the objective function of this optimal bidding model to obtain the bidding frequency regulation capacity, bidding frequency regulation capacity price, bidding frequency regulation mileage price of the integrated energy virtual power plant, and the frequency regulation capacity of the aggregated battery energy storage entity, aggregated thermal energy storage device entity, aggregated flexible load entity, and aggregated alternative load entity of the integrated energy virtual power plant.

[0060] 3) Build an optimization model for the integrated energy virtual power plant to participate in secondary frequency regulation under the intraday market mechanism. According to the winning bid results of the integrated energy virtual power plant in the day-ahead market, the market operation center optimizes the clearing based on the real-time frequency regulation demand. The integrated energy virtual power plant establishes an optimized aggregation objective function with the goal of maximizing revenue and achieving the best frequency regulation effect, solves the objective function of this optimization model, and obtains the frequency regulation mileage of the aggregated battery energy storage entity, aggregated heat storage equipment entity, aggregated flexible load entity, and aggregated alternative load entity of the integrated energy virtual power plant, as well as the final frequency regulation revenue obtained by the integrated energy virtual power plant.

[0061] 4) Use the frequency regulation mileage of the aggregated battery energy storage entity, aggregated heat storage equipment entity, aggregated flexible load entity, and aggregated alternative load entity of the integrated energy virtual power plant obtained in step 3) as the actual output of the virtual power plant participating in the secondary frequency regulation market. Set comparison cases to evaluate the mutual substitution ability between the power system resources and thermal system resources in the integrated energy virtual power plant, and focus on evaluating the impact of the mutual substitution effect of the integrated energy resources on the revenue ability of participating in market competition.

[0062] The content of the secondary frequency regulation market mechanism for the virtual power plant participation described in step 1) is as follows:

[0063] The secondary frequency regulation market operation center announces the frequency regulation capacity demand, performance demand, and frequency regulation mileage factor that the system needs to purchase in the frequency regulation market at 10:00 the day before operation (day-ahead). By predicting the load curve of the next operation day, 0.7% of the peak load and valley load are respectively taken as the frequency regulation demands for these two time periods.

[0064] The secondary frequency regulation market operation center classifies the frequency regulation resources into traditional frequency regulation resources and fast response frequency regulation resources according to the frequency regulation performance: Traditional frequency regulation resources are greatly restricted by the ramp rate and are mainly used to respond to traditional frequency regulation signals, including gas turbine units, coal-fired power units, etc.; Fast response frequency regulation can perform frequency regulation by instantaneously increasing or decreasing the output, is greatly restricted by energy, and mainly operates based on dynamic frequency regulation signals.

[0065] The virtual power plants and other bidding entities participating in the frequency regulation service submit bids, and their trading targets mainly include: capacity bid, mileage bid, and frequency regulation capacity. The mileage factor is used to adjust the bid according to the frequency regulation performance indicators of each resource, as shown in formulas (1-1) and (1-2).

[0066]

[0067]

[0068] In the formula and are the unadjusted capacity bid and mileage bid respectively, is the historical frequency modulation performance index of this frequency modulation resource, β l,t is the mileage factor, that is, the mileage call rate of this resource. and are the adjusted capacity bid and mileage bid respectively.

[0069] Pre-clearing is carried out at 2:00 pm on the day before. Based on the capacity price and mileage price, the market operation center considers the opportunity cost that the bidding entity cannot participate in the energy market due to participating in frequency modulation. Therefore, during the pre-clearing on the day before, the sorted price is obtained according to the predicted nodal marginal price of the real-time market and the bidding prices of each bidding entity. The pre-clearing is carried out in ascending order of the sorted price, and the clearing result is announced at 4:00 pm on the day before.

[0070] The calculation method of the sorted price is shown in formula (1-3).

[0071]

[0072] In the formula is the clearing sorted price, is the adjusted capacity bid, is the adjusted mileage bid, is the opportunity cost generated because the bidding entity cannot participate in the energy market due to participating in the frequency modulation market.

[0073] The clearing of the day-ahead market aims to minimize the cost of purchasing the required frequency modulation products, as shown in formula (1-4):

[0074]

[0075] In the formula, N T is the total number of scheduling times; t is the frequency modulation time, and its value is between 1-N T ; N l is the total number of bidding entities l, is the cost of purchasing the required frequency modulation products; and are the capacity price and mileage price of the frequency modulation of bidding entity l respectively; and are the frequency modulation capacity and frequency modulation mileage of bidding entity l respectively.

[0076] The intraday frequency modulation real-time market is mainly used to solve the problem of insufficient day-ahead ancillary services, and its operation mechanism is similar to that of the day-ahead market. During the clearing process, the capacity bid and mileage bid remain unchanged, the sorted price is recalculated according to the real-time electricity price, and the revenue settlement is carried out according to this price.

[0077]

[0078] In the formula, N Tis the total number of scheduling moments; t is the frequency regulation moment, and its value ranges from 1 to N T ; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of signals of the fast frequency regulation signal within the scheduling period, and its value ranges from 1 to N D ; N A is the total number of traditional frequency regulation signals within the scheduling period; A is the number of signals of the traditional frequency regulation signal within the scheduling period, and its value ranges from 1 to N A ; is the final profit obtained by the integrated energy virtual power plant in the frequency regulation market; is the nodal marginal price; is the average value of the nodal marginal price within the scheduling period; are respectively the clearing capacity price and mileage price of the integrated energy virtual power plant participating in the frequency regulation market; R egD , R egA are respectively the coefficients of the fast frequency regulation signal and the traditional frequency regulation signal in the intraday market; is the real-time frequency regulation mileage of the integrated energy virtual power plant; are respectively the real-time frequency regulation mileage of the integrated energy virtual power plant responding to the fast frequency regulation signal and the traditional frequency regulation signal; are respectively the winning bid capacity of the virtual power plant participating in the day-ahead market, the winning bid capacity adjusted according to the traditional frequency regulation signal, and the winning bid capacity used for calculation adjusted according to the fast frequency regulation signal.

[0079] The clearing mechanism of the intraday market requires that both the frequency regulation capacity and the frequency regulation mileage meet the frequency regulation requirements, that is:

[0080]

[0081]

[0082] In the formula, N l is the total number of bidders l, are respectively the clearing capacity and clearing mileage of the bidder l, is the frequency regulation demand capacity of the market, P t need,d is the frequency regulation demand mileage of the market.

[0083] The content of constructing the optimal bidding model for the integrated energy virtual power plant to participate in secondary frequency regulation under the day-ahead market mechanism described in step 2) is as follows:

[0084] The integrated energy virtual power plant constructs a bidding strategy by considering the operating status, scheduling cost and other information of distributed energy within each aggregation entity and the bidding information obtained from the market side. To obtain the operating parameters and aggregation cost information of each aggregation entity of the integrated energy virtual power plant, an aggregation model of the integrated energy virtual power plant is constructed.

[0085] 2-1) Aggregation Model of Virtual Power Plant in Integrated Energy System

[0086] 2-1-1) Flexible Load Model: Flexible loads can increase or decrease the load within a certain range according to the grid demand, and achieve energy exchange with the grid by participating in grid operation control.

[0087] The compensation cost of flexible loads is shown in Equation (2-1):

[0088]

[0089] In the formula, N T is the total number of scheduling moments, t is the frequency regulation moment, and its value is between 1 and N T . is the frequency regulation compensation amount of the nth flexible load cluster entity in the t-th period, is the electric power of the nth flexible load cluster entity participating in demand response in the t-th period. α n,t is the compensation amount coefficient of the flexible load entity, which is related to the time-of-use electricity price and the number and time of demand response in this period. μ n is the 0-1 state variable of load reduction, and 1 means reduction. γ n is the flexible load reduction coefficient.

[0090] The flexible load response grid control model is shown in Equations (2-2) and (2-3):

[0091]

[0092] In the formula, μ n is the 0-1 state variable of load reduction, and 1 means reduction. γ n is the flexible load reduction coefficient. is the flexible load reduction electric power, is the electric power of the flexible load after load reduction.

[0093]

[0094] In the formula, μ n is the 0-1 state variable of load n reduction, and 1 means reduction. μ t , μ t-1 are the 0-1 state variables of load reduction at t and t-1 moments respectively, is the shortest response duration for the load to participate in reduction.

[0095] 2-1-2) Replaceable Load Model:

[0096] The electrical and thermal loads that can replace the load are flexibly convertible, and the energy consumption demands of users can be replaced by different energy sources. By equipping a heat storage device in the integrated energy heat network system in the present invention, the flexibility of the replaceable load response and the competitiveness of the aggregation entity are increased. The operation models of the replaceable load are shown in Equations (2-4) and (2-5).

[0097]

[0098] In the formula, is the electrical (thermal) load before the response of the replaceable load j, is the electrical (thermal) load after the response of the replaceable load j, is the actual electrical (thermal) load participating in the response.

[0099] The output of the replaceable load shall not exceed the maximum load in this period and is restricted by a certain ratio.

[0100]

[0101] In the formula, is the electrical power of the replaceable load j responding to the frequency modulation change. is the thermal power of the replaceable load j responding to the frequency modulation change. γ j,t is the load conversion coefficient.

[0102] 2-1-3) Heat pump model:

[0103] The dynamic model of the heat pump is shown in Equation (2-6)

[0104]

[0105] In the formula, T a,k , T m,k and T 0 are the internal temperature, medium temperature and ambient temperature of the kth heat pump respectively. C a,k , C m.k are the air heat capacity and medium heat capacity respectively. R a,k , R m,k are the air thermal resistance and medium thermal resistance. T a,k and T m,k are the temperatures in the air and medium respectively. is the thermal power of the heat pump at time t.

[0106] The regulation process of the heat pump needs to satisfy the energy conversion relationship as shown in Equation (2-7)

[0107]

[0108] In the formula, is the thermal power of the heat pump at time t, is the electrical power of the heat pump at time t, For the heat supply efficiency of the heat pump.

[0109] 2-1-4) Battery energy storage model:

[0110] Battery energy storage power constraint conditions:

[0111]

[0112] In the formula, is the available capacity of the battery energy storage at time t, is the available capacity of the battery energy storage at time t-1, δ e,s is the self-discharge rate of the battery energy storage, is the charging power of the battery energy storage, is the discharging power of the battery energy storage, and are the charging efficiency and discharging efficiency of the battery energy storage respectively, and Δt is the charging and discharging time of the battery energy storage.

[0113] The state of charge (SOC) of the battery energy storage is expressed as:

[0114]

[0115] In the formula, SOC i,t is the state of charge of the i-th battery energy storage, is the available capacity of the i-th battery energy storage at time t, is the rated capacity of the i-th battery energy storage.

[0116]

[0117] In the formula, SOC i,t is the state of charge of the i-th battery energy storage, SOC i,t is the lower limit of the state of charge, is the upper limit of the state of charge.

[0118] The cycle life cost of the battery energy storage depends on the charge and discharge cycle behavior. Frequent charge and discharge will shorten the battery life. The battery life calculation is shown in Equation (2-11):

[0119]

[0120] In the formula, T C is the life of the battery energy storage. N DOD is the total number of cycles of the battery energy storage under constant D OD . N DOD,day is the average daily number of cycles of the battery energy storage.

[0121] D OD = 1 - SOC (2-12)

[0122] In the formula, SOC is the state of charge of the battery energy storage, and D OD is the depth of discharge.

[0123]

[0124] In the formula, N 100,eq is the number of cycles of the battery energy storage at a depth of discharge of 100%. N DOD is the total number of cycles of the battery energy storage at a constant D OD and k eq is the conversion coefficient.

[0125] ΔD OD = D OD,start - D OD,end (2-14)

[0126] In the formula, ΔD OD is the change in the depth of discharge of the energy storage device, D OD,start is the depth of discharge at the start of discharge of the energy storage device, and D OD,end is the depth of discharge at the end of discharge of the energy storage device.

[0127] Approximately, the depth of discharge of the energy storage cycle can be shown by Equation (2-15):

[0128]

[0129] In the formula, N 100,eq is the number of cycles of the battery energy storage at a depth of discharge of 100%. ΔD OD is the change in the depth of discharge of the energy storage device. k eq is the conversion coefficient.

[0130] The cost of the battery energy storage over its entire life cycle includes the operating cost, the operation and maintenance cost, and the life daily loss cost.

[0131]

[0132] In the formula, is the cost of the battery energy storage over its entire life cycle, is the life loss cost of the battery energy storage, is the operating cost of the battery energy storage, is the operation and maintenance cost of the battery energy storage.

[0133]

[0134] In the formula, t is the frequency modulation time, and its value is between 1 and N T inclusive. is the life daily loss cost of the battery energy storage, is the construction cost per unit capacity of the battery energy storage, is the rated capacity of the i-th battery energy storage, r is the investment interest rate, T C is the lifespan of the battery energy storage.

[0135]

[0136] In the formula, t is the frequency regulation time, and its value is between 1 and N T inclusive. is the operating cost of the battery energy storage, is the time-of-use electricity price, is the charging power of the battery energy storage, is the discharging power of the battery energy storage,

[0137]

[0138] In the formula, t is the frequency regulation time, and its value is between 1 and N T inclusive. is the operation and maintenance cost of the battery energy storage, is the rated capacity of the i-th battery energy storage, is the operation and maintenance coefficient.

[0139] 2-1-5) The structure of the heat storage equipment model is as Figure 3 shown. The heat storage equipment in the present invention includes an atmospheric pressure hot water tank and a heat exchanger, and the connection method to the heat network is indirect connection. The valve can be switched between different operation modes. During the heat storage process, valves C, B, E, F, and G are opened, and valves A and D are closed to allow the cold water in the water tank to pass through the heat exchanger. Hot water enters from the upper pipeline, and the same amount of cold water flows out from the lower pipeline, and the inclined temperature layer moves downward. During the heat release process, valves B and C are closed, and valves A, D, E, F, and G are opened. Cold water flows out from the bottom, and the same amount of hot water flows out from the upper pipeline, and the inclined temperature layer moves upward.

[0140]

[0141] In the formula, is the available capacity of the heat storage equipment at time t, ρ is the medium density, c is the medium specific heat capacity, is the volume of the heat storage equipment, is the medium temperature of the heat storage equipment, T co is the ambient temperature.

[0142]

[0143] In the formula, is the lost energy of the heat storage equipment, T in , T out are the inlet temperature and outlet temperature of the heat storage equipment, is the water flow rate, is the heat loss during the cycle.

[0144]

[0145] In the formula, is the heat loss during the cycle. h is the convective heat transfer coefficient, and W is the surface area of the heat storage device. is the medium temperature of the heat storage device, and T co is the ambient temperature.

[0146]

[0147] In the formula, are the available capacities of the heat storage device at time t and t - 1 respectively, is the energy loss of the heat storage device, are the charging and discharging efficiencies of the heat storage device respectively, are the charging and discharging powers of the heat storage device respectively. Δt is the charging and discharging time of the heat storage device.

[0148] Since the heat storage device has a long lifespan and a low price, the present invention only considers the operating cost of the heat storage device and the operation and maintenance cost of the heat storage device.

[0149]

[0150] In the formula, is the cost of the heat storage device considering the entire life cycle, is the operating cost of the heat storage device, is the operation and maintenance cost of the heat storage device.

[0151]

[0152] In the formula, N T is the total number of scheduling times, and t is the frequency modulation time, whose value is between 1 - N T inclusive. is the operation and maintenance cost of the heat storage device. is the unit construction cost of the heat storage device, is the rated capacity of the heat storage device, and T C,tes is the lifespan of the heat storage device, r is the investment interest rate, and ε 2 is the operation and maintenance coefficient.

[0153]

[0154] In the formula, N T is the total number of scheduling times, and t is the frequency modulation time, whose value is between 1 - N T inclusive. is the operating cost of the heat storage device, C op is the energy efficiency ratio of the heat storage device, They are the charging and discharging powers of the heat storage equipment respectively.

[0155] Under the secondary frequency regulation market mechanism, a virtual power plant aggregation model based on the integrated energy system that meets the secondary frequency regulation market demand is constructed:

[0156]

[0157] Where: N i is the total number of battery energy storages, and i is the battery energy storage serial number; N n is the total number of flexible loads, and n is the flexible load serial number; N j is the total number of replaceable loads, and j is the replaceable load serial number; is the total number of heat storage equipment, is the heat storage equipment serial number; N D is the total number of fast frequency regulation signals within the dispatching period; D is the number of signals of the fast frequency regulation signal within the dispatching period, and its value is between 1 - N D ; N A is the total number of traditional frequency regulation signals within the dispatching period; A is the number of signals of the traditional frequency regulation signal within the dispatching period, and its value is between 1 - N A ; They are the unit frequency regulation capacity costs of the aggregated battery energy storage, flexible load, replaceable load, and heat storage equipment of the integrated energy virtual power plant respectively; They are the unit frequency regulation mileage costs of the aggregated battery energy storage, flexible load, replaceable load, and heat storage equipment of the integrated energy virtual power plant respectively; They are the frequency regulation capacities of the battery energy storage, flexible load, replaceable load, and heat storage equipment respectively; They are the frequency regulation mileages of the battery energy storage, flexible load, replaceable load, and heat storage equipment respectively;

[0158] 2 - 2) Integrated energy virtual power plant optimization bidding model

[0159] Based on the established integrated energy virtual power plant model, on the basis of the secondary frequency regulation day - ahead market mechanism determined in step 1), integrated energy virtual power plant optimization bidding is carried out. The block diagram of the integrated energy virtual power plant participating in the secondary frequency regulation market model is as Figure 4 shown. Determine the revenue of the virtual power plant participating in the day - ahead secondary frequency regulation market bidding:

[0160]

[0161] Where: N T is the total number of dispatching moments; t is the frequency regulation moment, and its value is between 1 - N T ; They are the bidding capacity price and mileage price of the integrated energy virtual power plant respectively; They are the frequency regulation capacity and frequency regulation mileage of the integrated energy virtual power plant respectively;

[0162] According to the preliminary evaluation of the frequency regulation effect based on the regulation demand information announced in the day-ahead secondary frequency regulation market, as follows

[0163]

[0164] Among them: N D is the total number of fast frequency regulation signals within the dispatching period; D is the number of fast frequency regulation signals within the dispatching period, and its value is between 1 - N D ; N A is the total number of traditional frequency regulation signals within the dispatching period; A is the number of traditional frequency regulation signals within the dispatching period, and its value is between 1 - N A ; They are the frequency regulation mileage of the integrated energy virtual power plant in response to fast frequency regulation signals and traditional frequency regulation signals respectively, They are the frequency regulation capacity of the integrated energy virtual power plant in response to fast frequency regulation signals and traditional frequency regulation signals respectively; R egD , R egA are the coefficients of fast frequency regulation signals and traditional frequency regulation signals in the intraday market respectively; ω D , ω A is the frequency regulation error penalty coefficient;

[0165] The objective function of the day-ahead optimization bidding model of the integrated energy virtual power plant is as follows:

[0166] maxF 1 = ω 1 f 1 - ω 2 f 2 - ω 3 f 3

[0167] (2 - 30)

[0168] Among them: ω 1 , ω 2 , ω 3 are the function penalty coefficients respectively.

[0169] In addition to meeting the constraints of each aggregation entity of the integrated energy virtual power plant itself, the other constraints to be met are as follows:

[0170]

[0171] In the formula, N i is the total number of battery energy storages, and i is the serial number of the battery energy storage. N n is the total number of flexible loads, and n is the serial number of the flexible load. N j is the total number of replaceable loads, and j is the serial number of the replaceable load. is the total number of heat storage devices, is the serial number of the heat storage device. is the aggregation incentive coefficient of the integrated energy virtual power plant capacity. is the capacity price of the integrated energy virtual power plant. are respectively the capacity costs of the integrated energy virtual power plant aggregating battery energy storage, flexible load, alternative load, and heat storage devices. are respectively the frequency regulation capacities of the integrated energy virtual power plant aggregating battery energy storage, flexible load, alternative load, and heat storage devices.

[0172]

[0173] In the formula, N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of signals of the fast frequency regulation signal within the scheduling period, and its value is between 1 - N D ; N A is the total number of traditional frequency regulation signals within the scheduling period. A is the number of signals of the traditional frequency regulation signal within the scheduling period, and its value is between 1 - N A ; N i is the total number of battery energy storage, and i is the serial number of the battery energy storage. N n is the total number of flexible loads, and n is the serial number of the flexible load. N j is the total number of alternative loads, and j is the serial number of the alternative load. is the total number of heat storage devices, is the serial number of the heat storage device. is the mileage aggregation incentive coefficient of the integrated energy virtual power plant. is the mileage price of the integrated energy virtual power plant. are respectively the frequency regulation mileages of battery energy storage, flexible load, alternative load, and heat storage devices. are respectively the adjusted capacity costs of the integrated energy virtual power plant aggregating battery energy storage and flexible load. are respectively the adjusted unit capacity costs of the integrated energy virtual power plant aggregating alternative load and heat storage devices.

[0174]

[0175] In the formula, N i is the total number of battery energy storage, and i is the serial number of the battery energy storage. N n is the total number of flexible loads, and n is the serial number of the flexible load. N j is the total number of alternative loads, and j is the serial number of the alternative load. is the total number of heat storage devices, is the serial number of the heat storage device. are respectively the bidding capacities of battery energy storage, flexible load, alternative load, and heat storage devices. is the frequency regulation capacity of the integrated energy virtual power plant.

[0176]

[0177] In the formula, : N T is the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 - N T ; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of fast frequency regulation signals within the scheduling period, and its value is between 1 - N D ; N A is the total number of traditional frequency regulation signals within the scheduling period. A is the number of traditional frequency regulation signals within the scheduling period, and its value is between 1 - N A ; k is the serial number of the fast frequency regulation signal in the t-th scheduling period, and its value is between t - 1 and N D t / N T ; s is the serial number of the traditional frequency regulation signal in the t-th scheduling period, and its value is between t - 1 and N A t / N T ; N i is the total number of battery energy storages, and i is the serial number of the battery energy storage. N n is the total number of flexible loads, and n is the serial number of the flexible load. N j is the total number of replaceable loads, and j is the serial number of the replaceable load. is the total number of heat storage devices, is the serial number of the heat storage device. is the frequency regulation mileage of the integrated energy virtual power plant. are the frequency regulation mileages of the battery energy storage and flexible load that respond to the fast frequency regulation signal respectively. are the frequency regulation mileages of the replaceable load and heat storage device that respond to the traditional frequency regulation signal respectively.

[0178]

[0179] In the formula, are the frequency regulation mileage and frequency regulation capacity of each aggregation entity ξ respectively.

[0180] Solving the above objective function, the competitive bidding frequency regulation capacity of the integrated energy virtual power plant is obtained Competitive bidding frequency regulation capacity price Competitive bidding frequency regulation mileage price and the frequency regulation capacities of the integrated energy virtual power plant aggregating battery energy storage entities, heat storage device entities, flexible load entities, and replaceable load entities

[0181] The content of the optimization model for the integrated energy virtual power plant to participate in secondary frequency regulation under the intraday market mechanism described in step 3) is as follows:

[0182] In the intraday market of the secondary frequency regulation market, the optimization model for the integrated energy virtual power plant to participate in the intraday market of secondary frequency regulation is as follows:

[0183] Determine the revenue obtained by the integrated energy virtual power plant in the intraday market of secondary frequency regulation:

[0184]

[0185]

[0186] Where: N T is the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 - N T ; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of signals of the fast frequency regulation signal within the scheduling period, and its value is between 1 - N D ; N A is the total number of traditional frequency regulation signals within the scheduling period. A is the number of signals of the traditional frequency regulation signal within the scheduling period, and its value is between 1 - N A ; R egD , R egA are the coefficients of the fast frequency regulation signal and the traditional frequency regulation signal in the intraday market respectively; are the bid capacity price and the mileage price of the integrated energy virtual power plant respectively; is the nodal marginal price of the location where the virtual power plant is located; is the winning bid capacity of the virtual power plant participating in the day-ahead market, and its value is obtained after day-ahead clearing by the secondary frequency regulation day-ahead market according to the virtual power plant's bid frequency regulation capacity in step 2) ; are the winning bid capacities for calculation adjusted according to the traditional frequency regulation signal and the winning bid capacities for calculation adjusted according to the fast frequency regulation signal respectively; are the mileage of the integrated energy virtual power plant participating in frequency regulation, the frequency regulation mileage of responding to the fast frequency regulation signal, and the frequency regulation mileage of participating in the traditional frequency regulation signal respectively;

[0187] The allocation of the frequency regulation mileage of each aggregation entity is as follows:

[0188]

[0189] Where: N T is the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 - N T ; N Dis the total number of fast frequency modulation signals within the scheduling period; D is the number of fast frequency modulation signals within the scheduling period, and its value ranges from 1 to N D between; N A is the total number of traditional frequency modulation signals within the scheduling period. A is the number of traditional frequency modulation signals within the scheduling period, and its value ranges from 1 to N A between; N i is the total number of battery energy storages, and i is the battery energy storage serial number; N n is the total number of flexible loads, and n is the flexible load serial number; N j is the total number of replaceable loads, and j is the replaceable load serial number; is the total number of heat storage devices, is the heat storage device serial number; are respectively the unit capacity costs of the aggregated battery energy storage, flexible load, replaceable load, and heat storage device in the integrated energy virtual power plant; are respectively the unit mileage costs of the aggregated battery energy storage, flexible load, replaceable load, and heat storage device in the integrated energy virtual power plant; are respectively the bidding frequency modulation capacity and bidding frequency modulation mileage of the integrated energy virtual power plant; are respectively the frequency modulation capacities of the battery energy storage, flexible load, replaceable load, and heat storage device; are respectively the frequency modulation mileages of the battery energy storage, flexible load, replaceable load, and heat storage device;

[0190] The objective function of the optimization model for the integrated energy virtual power plant to participate in the secondary frequency modulation intraday market is as follows:

[0191] maxF 2 = ω 4 f 4 - ω 5 f 5

[0192] (3 - 3)

[0193] where: ω 4 , ω 5 are respectively the function penalty coefficients;

[0194] Solve the objective function of the above optimization model to obtain the frequency modulation mileages of the aggregated battery energy storage entity, aggregated heat storage device entity, aggregated flexible load entity, and aggregated replaceable load entity in the integrated energy virtual power plant and the frequency modulation revenue f finally obtained by the integrated energy virtual power plant 4 .

[0195] The integrated energy virtual power plant conducts real-time simulation to evaluate the frequency modulation effect considering the time-delay characteristics of aggregated resources. The model for the integrated energy virtual power plant to conduct real-time simulation for frequency modulation effect evaluation considering the time-delay characteristics of aggregated resources is shown in Equation (3-4):

[0196]

[0197] In the formula, t is the frequency modulation time. Δf is the frequency offset, is the differential of the frequency offset at time t. H eq is the system inertia constant. D is the system load damping coefficient. τ es , τ cut , τ ctrl , τ rep , τ tes are the time-delay coefficients of battery energy storage, flexible load regulation, flexible load curtailment, replaceable load, and heat storage equipment respectively. P t e,s,R , P t fle,R , P t tes,R are the frequency modulation mileage of battery energy storage, flexible load, replaceable load, and heat storage equipment at time t respectively.

[0198] Step 4) Evaluation of the mutual substitution ability of comprehensive resources

[0199] Taking the frequency modulation mileage of the aggregated battery energy storage entity, aggregated heat storage equipment entity, aggregated flexible load entity, and aggregated replaceable load entity of the integrated energy virtual power plant obtained in Step 3) as the actual output of the virtual power plant participating in the secondary frequency modulation market. And a comparison scenario without thermal system resources is constructed. According to the winning bid results of the integrated energy virtual power plant participating in the day-ahead frequency modulation market bidding and calculating the final income of the virtual power plant in the comparison scenario according to the real-time frequency modulation mileage in the intraday market, the mutual substitution effect of integrated energy resources is evaluated.

[0200] Research materials:

[0201] This paper improves the 141-node distribution network system and the 33-node heat network to form an integrated energy system as the aggregation area of virtual power plants. As Figure 3 shown, battery energy storage is configured at nodes 45, 68, 104, 112, and 130 in the distribution network. Nodes 4, 12, 28, 29, 42, 55, 58, 61, 79, 85, 91, 124, and 136 are selected as flexible load nodes. Nodes 32, 47, 72, 109, and 137 are selected as replaceable load nodes. Heat storage devices are configured at nodes 1, 15, 24, 28, and 31 in the heat network, and heat pumps are coupled and configured at the same time.

[0202] Under the frequency modulation market mechanism established in step 2), substitute the above Figure 1 and Figure 2 frequency modulation signal data and frequency modulation mileage data into the integrated energy virtual power plant participation in the secondary frequency modulation market optimization bidding model obtained in step 3), and optimize and solve to obtain the bidding target and winning bid capacity of the integrated energy virtual power plant participating in the secondary frequency modulation market, as Figure 6 shown. Further obtain the secondary frequency modulation capacity situation of each aggregation entity of the integrated energy virtual power plant as Figure 7 shown.

[0203] Apply the virtual power plant optimization bidding model proposed in step 3) to participate in the frequency modulation market bidding, substitute the bidding results into step 4), perform power distribution among each aggregation entity, and calculate the actual revenue of the virtual power plant according to the actual output situation of the integrated energy virtual power plant in the intraday market as shown in Table 1, and calculate the cost and revenue situation of each aggregation entity of the virtual power plant participating in frequency modulation as shown in Table 2. The simulation of the frequency modulation effect of the integrated energy virtual power plant in the intraday market is as Figure 8 shown.

[0204] Table 1

[0205]

[0206] Table 2

[0207]

[0208] To compare and evaluate the influence of the complementary effect of electric and thermal resources in the integrated energy virtual power plant, the following comparison scenarios are set to analyze the influence of the heat storage device on the economy of the integrated energy virtual power plant and the life of the electricity storage equipment.

[0209] Comparison scenario: Only consider that the adjustable resources of the virtual power plant aggregated power system (i.e., battery energy storage and flexible load) participate in the secondary frequency modulation market.

[0210] Figure 9 shows the energy state of the battery energy storage and the heat storage device under different scenarios of applying the present invention, where Figure 9 (a) and Figure 9 (b) respectively represent the energy state of the battery energy storage and the heat storage device participating in the secondary frequency modulation intraday market in the original scenario, while Figure 9 (c) represents the energy state of the battery energy storage in the comparison scenario. It can be seen that the operation of the adjustable resources in the thermal system can keep the energy state of the battery energy storage in a relatively healthy state, effectively reducing the output pressure of the battery energy storage, and having a certain substitution and complementarity for the battery energy storage. Figure 10 (a) shows the battery energy storage life under different scenarios, Figure 10(b) shows the net benefits of each aggregator participating in the secondary frequency regulation market under different scenarios. It can be seen that the participation of adjustable resources in the thermal system effectively improves the lifespan of battery energy storage and greatly enhances the economy of virtual power plants.

[0211] Compared with traditional power system virtual power plants, the integrated energy virtual power plant proposed in the present invention aggregates electric and thermal resources simultaneously, enabling the complementary characteristics between different energies in terms of economy and performance to be manifested, and greatly enhancing the market competitiveness of virtual power plants in participating in secondary frequency regulation.

[0212] Although the present invention has been described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many variations without departing from the purpose of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A secondary frequency regulation method for a virtual power plant considering integrated energy resources, characterized in that, it includes the following steps: 1) Construct a market mechanism for the virtual power plant to participate in secondary frequency regulation, including a day-ahead market model and an intraday market model; 2) Construct an optimization bidding model for the integrated energy virtual power plant to participate in secondary frequency regulation under the day-ahead market mechanism, solve the objective function of the optimization bidding model, and obtain the bidding frequency regulation capacity, the price of the bidding frequency regulation capacity, the price of the bidding frequency regulation mileage, and the frequency regulation capacities of the aggregated battery energy storage entity, the aggregated heat storage equipment entity, the aggregated flexible load entity, and the aggregated alternative load entity of the integrated energy virtual power plant; 3) Construct an optimization model for the integrated energy virtual power plant to participate in secondary frequency regulation under the intraday market mechanism, solve the objective function of the optimization model, and obtain the frequency regulation mileage of the aggregated battery energy storage entity, the aggregated heat storage equipment entity, the aggregated flexible load entity, and the aggregated alternative load entity of the integrated energy virtual power plant and the final frequency regulation revenue obtained by the integrated energy virtual power plant; At the same time, the integrated energy virtual power plant considers the time-delay characteristics of the aggregated resources for real-time simulation to evaluate the frequency regulation effect; 4) Use the frequency regulation mileage of the aggregated battery energy storage entity, the aggregated heat storage equipment entity, the aggregated flexible load entity, and the aggregated alternative load entity of the integrated energy virtual power plant obtained in step 3) as the actual output of the virtual power plant participating in the secondary frequency regulation market; The specific steps of step 2) include: 2-1) Determine the revenue of the virtual power plant participating in the day-ahead secondary frequency regulation market bid: Where: N T is the total number of scheduling times; t is the frequency regulation time, and its value is between 1 and N T ; are the competitive frequency regulation capacity price and the competitive frequency regulation mileage price of the integrated energy virtual power plant respectively; are the competitive frequency regulation capacity and the competitive frequency regulation mileage of the integrated energy virtual power plant respectively; 2-2) Construct a virtual power plant model based on the integrated energy system that meets the requirements of the secondary frequency regulation market under the secondary frequency regulation market mechanism; Where: N T is the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 and N T ; N i is the total number of battery energy storages, and i is the battery energy storage serial number; N n is the total number of flexible loads, and n is the flexible load serial number; N j is the total number of alternative loads, and j is the alternative load serial number; is the total number of heat storage devices, is the heat storage device serial number; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of signals of the fast frequency regulation signal within the scheduling period, and its value is between 1 and N D ; N A is the total number of traditional frequency regulation signals within the scheduling period; A is the number of signals of the traditional frequency regulation signal within the scheduling period, and its value is between 1 and N A ; are respectively the unit competitive bidding frequency regulation capacity costs of the integrated energy virtual power plant aggregating battery energy storages, flexible loads, alternative loads, and heat storage devices; are respectively the unit competitive bidding frequency regulation mileage costs of the integrated energy virtual power plant aggregating battery energy storages, flexible loads, alternative loads, and heat storage devices; are respectively the frequency regulation capacities of the battery energy storage, flexible load, alternative load, and heat storage device; are respectively the frequency regulation mileages of the battery energy storage, flexible load, alternative load, and heat storage device; 2-3) According to the preliminary evaluation of the frequency regulation effect based on the regulation demand information announced in the day-ahead secondary frequency regulation market, as follows Wherein: are the frequency regulation mileage of the integrated energy virtual power plant responding to the fast frequency regulation signal and the traditional frequency regulation signal respectively, are the frequency regulation capacities of the integrated energy virtual power plant responding to the fast frequency regulation signal and the traditional frequency regulation signal respectively; R egD , R egA are the fast frequency regulation signal and traditional frequency regulation signal coefficients in the intraday market respectively; ω D , ω A is the frequency regulation error penalty coefficient; The objective function of the integrated energy virtual power plant day-ahead optimization bidding model is as follows: maxF 1 = ω 1 f 1 - ω 2 f 2 - ω 3 f 3 Where: ω 1 , ω 2 , ω 3 are respectively the function penalty coefficients, Solve the above objective function to obtain the bidding frequency regulation capacity of the integrated energy virtual power plant Bidding frequency regulation capacity price Bidding frequency regulation mileage price And the frequency regulation capacities of the integrated energy virtual power plant aggregating battery energy storage entities, aggregating flexible load entities, aggregating alternative load entities, and aggregating thermal energy storage equipment entities The specific steps of step 3) include: 3-1) Determine the revenue obtained by the integrated energy virtual power plant in the secondary frequency regulation intraday market: Where: N T is the total number of scheduling moments; t is the frequency regulation moment, and its value is between 1 and N T ; N D is the total number of fast frequency regulation signals within the scheduling period; D is the number of fast frequency regulation signals within the scheduling period, and its value is between 1 and N D ; N A is the total number of traditional frequency regulation signals within the scheduling period; A is the number of traditional frequency regulation signals within the scheduling period, and its value is between 1 and N A ; R egD , R egA are the coefficients of fast frequency regulation signals and traditional frequency regulation signals in the intraday market respectively; are the bidding frequency regulation capacity price and the bidding frequency regulation mileage price of the integrated energy virtual power plant respectively; is the nodal marginal price of the location where the virtual power plant is located; is the winning bid capacity of the virtual power plant participating in the day-ahead market, and its value is obtained after day-ahead clearing by the secondary frequency regulation day-ahead market according to the bidding frequency regulation capacity of the virtual power plant in step 2) ; are the winning bid capacities for calculation adjusted according to traditional frequency regulation signals and the winning bid capacities for calculation adjusted according to fast frequency regulation signals respectively; are the mileage of the integrated energy virtual power plant participating in frequency regulation, the frequency regulation mileage of responding to fast frequency regulation signals, and the frequency regulation mileage of participating in traditional frequency regulation signals respectively; 3-2) Allocate the frequency regulation mileage of each aggregated entity, as follows: Where: N i is the total number of battery energy storages, and i is the serial number of the battery energy storage; N n is the total number of flexible loads, and n is the serial number of the flexible load; N j is the total number of replaceable loads, and j is the serial number of the replaceable load; is the total number of heat storage devices, and is the serial number of the heat storage device; are respectively the unit competitive bidding frequency regulation capacity costs of the integrated energy virtual power plant aggregating battery energy storage, flexible load, replaceable load, and heat storage device; are respectively the unit competitive bidding frequency regulation mileage costs of the integrated energy virtual power plant aggregating battery energy storage, flexible load, replaceable load, and heat storage device; are respectively the competitive bidding frequency regulation capacity and competitive bidding frequency regulation mileage of the integrated energy virtual power plant; are respectively the frequency regulation capacities of the battery energy storage, flexible load, replaceable load, and heat storage device; are respectively the frequency regulation mileages of the battery energy storage, flexible load, replaceable load, and heat storage device; The objective function of the integrated energy virtual power plant participating in the secondary frequency regulation intraday market optimization model is as follows: maxF 2 = ω 4 f 4 - ω 5 f 5 Where: ω 4 and ω 5 are the function penalty coefficients respectively;​ (3-4) Solve the objective function of the above optimization model to obtain the frequency regulation mileage of the integrated energy virtual power plant aggregating the battery energy storage entity, the aggregated flexible load entity, the aggregated alternative load entity, and the aggregated heat storage equipment entity and the frequency regulation revenue f finally obtained by the integrated energy virtual power plant 4 .

2. The virtual power plant secondary frequency regulation method according to claim 1, characterized in that, In step 1), the day-ahead market model is: in the day-ahead market, the operation center announces the frequency regulation capacity and frequency regulation performance requirements, and each bidding entity participates in the day-ahead market bid with the capacity price, mileage price, and frequency regulation capacity as the bidding targets based on its own optimal operation strategy; The operation center clears with the goal of minimizing the capacity cost to meet the secondary frequency regulation demand, and announces the winning capacity, capacity price, and mileage price of each bidding entity; The intraday market model is: in the intraday market, each bidding entity optimizes and clears according to the winning bid results in the day-ahead market with the goal of minimizing the real-time frequency regulation cost on the operation day. Each winning bid entity outputs according to the clearing results and conducts a joint revenue settlement for the day-ahead and intraday markets at the end of the operation day.

3. The virtual power plant secondary frequency regulation method according to claim 1, characterized in that, In steps 2) and 3), the CPLEX solver is used to solve the objective function according to the time scale until an optimal solution is generated.

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