Regional integrated energy system transaction decision-making method considering participation in electric energy-reserve market

Abstract

The invention discloses a regional integrated energy system transaction decision-making method considering participation in an electric energy-standby market, and the method comprises the steps: proposing an architecture of the regional integrated energy system participating in the electric energy-standby market, and introducing a transaction mode and a service mechanism of the regional integrated energy system in the market; a Latin hypercube sampling method and a rapid previous elimination technology are adopted to solve the randomness problem of wind and light renewable energy output in the regional integrated energy system; a reserve capacity incentive mechanism is introduced, and a target function considering maximization of the regional integrated energy system participating in the electric energy-reserve market income is constructed; corresponding constraint conditions are constructed, a regional integrated energy system transaction decision method considering participation in the electric energy-reserve market is established, a solver is used for solving, and an optimal strategy is obtained; the method solves the problem of insufficient power supply of a regional power system caused by sudden increase of a short-term load peak value and increase of a standby gap of a power grid, and benefits of a power grid side and a user side are improved.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and more specifically, relates to a transaction decision-making method for a regional integrated energy system taking into account participation in an electric energy-reserve market. Background Art

[0002] In recent years, with the continuous grid connection of a high proportion of renewable energy and the continuous increase in short-term load peaks, the operation of the power grid is facing problems such as peak regulation, frequency regulation, and ramping. The gap in power grid reserve is increasing, the demand of the power system cannot be met, and the problem of supply and demand balance needs to be further explored. Demand-side resources need to be further tapped to provide sufficient reserve and flexible scheduling for the power grid. Therefore, the concept of integrated energy scheduling has emerged. The regional integrated energy system is a combination of various distributed resources, energy storage systems, flexible loads and other demand-side resources. It can flexibly perform energy scheduling such as frequency regulation and peak regulation, and has abundant reserve capacity. As one of the important means to enhance the regulation capacity of the power system, the regional integrated energy system plays an important role in alleviating regional power tensions and has broad market prospects. Therefore, it is of great theoretical and practical significance to promote the participation of regional integrated energy systems in the transaction decision-making of the electric energy-reserve market and realize the flexible scheduling and energy trading of integrated energy.

[0003] In recent years, research and practice have usually only considered energy storage, electric vehicles, and high-proportion new energy in various power market transaction decisions, lacking a complete integrated energy system to participate in grid interaction, resulting in insufficient demand-side resources and poor economic efficiency of various integrated energy interactions. For example, in some related technologies, with the photovoltaic storage system as the object, a method for the day-ahead bidding decision-real-time power adjustment of the photovoltaic storage system is proposed. By adjusting the real-time power of the energy storage charging and discharging, the uncertainty of the real-time output of photovoltaic power generation is dealt with, and the overall benefits of the photovoltaic storage system are increased; in other related technologies, in order to fully tap the response potential of electric vehicles, with electric vehicle aggregators as the main body, an optimized scheduling model for electric vehicle aggregators to participate in energy-frequency modulation auxiliary services is constructed, and the basic response incentive and response compensation method for electric vehicle users is designed. Reasonable incentive prices maximize the benefits of aggregators, and users can also obtain certain benefits by participating in different types of auxiliary services. However, in general, the above existing achievements only consider the dispatch and transaction of a single subject in the power market. There is still a lot of dispatch space for demand-side resources, and there is a lack of coordination between various resources. The investment in reserve capacity on the user side is ignored, and the incentive for demand-side reserve capacity is insufficient. Therefore, it is of great significance to pay attention to the participation of regional integrated energy systems in joint trading decisions in the electricity and reserve markets, aggregate various types of distributed energy, energy storage, hydrogen energy cascade utilization systems and various flexible loads in regional integrated energy systems, study the reserve capacity incentive mechanism to stimulate the demand-side resource scheduling potential, and combine the electricity and reserve markets to maximize the benefits on the grid and user sides. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention proposes a transaction decision method for a regional integrated energy system that considers participating in the electric energy-reserve market. The present invention solves the problem of tight power supply in the regional power system caused by the sudden increase in short-term load peak and the increase in the grid reserve gap, fully taps the resource potential on the demand side, enables the regional integrated energy system to participate in the electric energy-reserve market joint market, and conducts transaction decisions and flexible scheduling of diversified energy, which is conducive to improving the benefits of the grid side and the user side.

[0005] To achieve the above object, the present invention provides a method for making trading decisions for a regional integrated energy system that considers participating in an electric energy-reserve market, comprising the following steps:

[0006] Step 1: Propose a framework for regional integrated energy systems to participate in the electric energy-reserve market, and introduce its trading model and service mechanism in the electric energy-reserve market, including:

[0007] The Regional Integrated Energy System (RIES) studied includes photovoltaic, wind power, gas boilers, cogeneration devices, hydrogen energy cascade systems, electric heat and gas hydrogen storage devices, and various demand-side loads. RIES has its own network structure to manage and dispatch its internal resources, and can also participate in energy transactions in the electricity market as an independent entity. The electric energy market and the reserve market are managed by an independent system operator (ISO). RIES purchases and sells electricity from the electric energy market to earn the difference, and can also provide reserve capacity to the reserve market to make a profit. It is proposed that RIES participate in the electric energy-reserve market architecture,

[0008] RIES interacts with the electricity energy-reserve market to conduct energy declaration and resource scheduling to obtain corresponding benefits. RIES covers various types of demand-side resources downward, signs reserve contracts with them, provides reserve incentive fees to demand-side resources, and supports and encourages demand-side resources to participate in electricity market services.

[0009] In the power market environment, RIES participates in power market transaction decisions as an independent entity. RIES flexibly declares the purchase / sale of electric power to the electric energy market based on its own energy dispatching situation, and the power generation side reports the quantity without quoting. RIES provides backup to the backup market and declares backup capacity. The backup market issues backup events based on the distribution curve of its historical backup data, and the probability of calling backup is normally distributed. RIES has a probability of failure to provide backup services. The power trading center and the dispatching agency make joint decisions on the electric energy-backup market based on the collected quotation information, the load of the entire system, and the demand for backup services. By coordinating RIES to participate in multiple types of market transactions, the optimal decision is made to maximize the benefits of RIES itself.

[0010] Step 2: Propose the Latin hypercube method and fast previous generation elimination technology to solve the problem of randomness of wind and solar resource output in regional integrated energy systems, including:

[0011] A multi-scenario approach is used to describe the uncertainty of wind and solar resources in RIES. Taking wind power resources as an example, we first assume that wind power follows a normal distribution N(μ,σ 2 ), μ is the expected value of the predicted value, and σ is the percentage of its fluctuation. Then, Latin hypercube sampling is used to generate a large number of wind power output scenarios subject to probability distribution constraints, and the generated random scenarios are reduced using the fast predecessor elimination technology based on probability distance. Finally, the wind power output scenarios with corresponding probabilities after reduction are obtained.

[0012] (1) Latin Hypercube Sampling Scene Generation

[0013] Latin hypercube sampling can more accurately translate the probability distribution characteristics of the sampled data. Assume that there are N random variables K1 K N , where K N The cumulative probability distribution function of is:

[0014] F N =F N (K N ) (1)

[0015] Assuming the sampling size is R, then K N The adoption steps are as follows:

[0016] 1) F N The distribution curve is divided into R intervals, and the range of each interval is the same as 1 / R.

[0017] 2) Extract a number in each interval. The sampling point of the i-th interval is K Ni , its cumulative probability P Ni for:

[0018] P Ni =(1 / R)ri +(i-1) / R (2)

[0019] In the formula, r i is a random number in the interval [0,1].

[0020] 3) P Ni Substitution Function, get the corresponding interval sampling value K Ni

[0021]

[0022] 4) Sampling R times by the above method will generate K N R sample values ​​of .

[0023] 5) Generate an N×R dimensional matrix, randomly sort the rows and generate R random scenes.

[0024] (2) Rapid elimination of previous generation technology scenarios

[0025] The rapid predecessor elimination technique usually generates several scenarios to consider the majority of the occurrence states of uncertain parameters in the uncertainty modeling process and introduces a scenario reduction method to select candidate scenarios from all the scenarios generated in the system. After the Latin hypercube sampling method generates a large-scale resource scenario set S, the rapid predecessor elimination technique based on probability distance is used to reduce the number of large-scale scenarios. The steps of this technical method are as follows:

[0026] 1) Compute the geometric distance between each pair of scenes s and s' in S.

[0027] 2) Select the scene d with the smallest sum of probability distances to the remaining scenes.

[0028] 3) Replace scene d with scene r in S that has the closest geometric distance to scene d, add the probability of scene d to the probability of scene r, eliminate scene d, and form a new wind power resource scene set S'.

[0029] 4) Determine whether the number of remaining scenes meets the requirement; if not, repeat steps 1) to 3); if so, end scene reduction.

[0030] The uncertainty handling of photovoltaic resources can also be described using the above-mentioned multi-scenario generation and reduction method.

[0031] Step 3: Under the framework of step 1, combined with the wind and solar processing in step 2, introduce a reserve capacity incentive mechanism to construct an objective function that considers maximizing the benefits of the regional integrated energy system participating in the electric energy-reserve market, including:

[0032] The main goal is to maximize the benefits of the regional integrated energy system participating in the electric energy-reserve market. Therefore, the objective function RIES benefit G can be expressed as follows:

[0033]

[0034] Where: π w is the standardized probability of the wth scenario occurring, %; is the income obtained by RIES in the electric energy market exchange at time t under the w-th scenario, RMB; is the revenue obtained by RIES in the backup market at time t under the w-th scenario, RMB; is the power generation cost of RIES at time t under the w-th scenario, RMB; Energy storage cost at time t in the wth scenario, yuan; is the cost of demand response for load reduction within RIES at time t under the w-th scenario, RMB; ξ is the reserve capacity incentive.

[0035] (1) Electricity market revenue

[0036] RIES earns revenue by purchasing / selling electricity in the electricity market through its own energy dispatch.

[0037]

[0038] Where: P is the clearing price of the electricity market at time t; t RIES,in , P t RIES,out They are the electricity purchased and sold by RIES in the electricity market.

[0039] (2) Reserve market income

[0040] RIES earns revenue in the reserve market by reserving reserve capacity and actually providing reserve service electricity.

[0041]

[0042] Where: The price of reserved reserve capacity provided by RIES to the reserve market at time t; are the reserve market clearing price at time t and the penalty price for RIES not providing reserve services; is the probability of the system calling the standby at time t, which forms a normal distribution law based on historical data; They are the reserve capacity provided by RIES to the reserve market; is the probability of failure of RIES to provide backup service at time t.

[0043] (3) Reserve Capacity Incentive

[0044] In order to encourage the enthusiasm of resource dispatch on the demand side, a reserve capacity incentive mechanism is introduced. On the premise of satisfying its own operation, the more reserved reserve capacity RIES declares to the reserve market, the more cost incentive compensation will be given to power generation enterprises, further improving the success probability of RIES actually providing reserve services to the reserve market. The specific ratio will be adjusted according to the power market environment.

[0045]

[0046] Where: C RIES,RC The cost of providing reserved spare capacity for RIES; P RIES,RC The reserved reserve capacity reported by RIES to the reserve market; Q RIES is the RIES capacity.

[0047] (4) RIES power generation cost

[0048] The cost of renewable energy generation is relatively low and is ignored in the objective function of RIES. Distributed power sources in RIES include combined heat and power generation units and hydrogen fuel cell generation in the hydrogen energy cascade system.

[0049]

[0050] Where: is the unit cost coefficient; They are respectively the power generation of the cogeneration unit and the hydrogen fuel cell in scenario w at time t.

[0051] (5) Energy storage costs

[0052] Energy storage costs include the charging and discharging costs of the energy storage system as well as the operation and maintenance costs.

[0053]

[0054] Where: are the energy released and charged by the energy storage system at time t; η ES,ch , η ES,dis They are the charging and discharging efficiency of energy storage respectively; The unit charging and discharging cost of the energy storage system; is the unit operation and maintenance cost of the energy storage system; Q ES is the capacity of the energy storage system.

[0055] (6) Can reduce load costs

[0056] The load can be reduced during peak electricity consumption to alleviate the peak electricity consumption; during low electricity consumption periods, it can increase output and promote the consumption of clean energy.

[0057]

[0058] Where: is the load reduction amount aggregated by RIES in scenario w at time t; a1, a2, a3 are the quadratic, linear and constant term parameters of the load reduction cost function.

[0059] Step 4: Based on step 3, construct corresponding constraints, establish the optimal decision-making model of the regional integrated energy system considering the participation in the electric energy-reserve market, and use the solver to solve it to obtain the optimal strategy, including:

[0060] Considering that RIES is equipped with wind and solar power units, gas boilers, cogeneration units, hydrogen energy cascade utilization systems, energy storage systems and various flexible loads, RIES participates in the energy trading of the electricity-reserve market as an independent entity.

[0061] The constraints for RIES to participate in the power energy-reserve market transaction decision-making include output upper and lower limit constraints, ramp constraints, safety constraints and conventional constraints. The constraints should also include:

[0062] (1) Unit constraints

[0063] 1) Unit climbing constraints

[0064]

[0065] Where: They are the upper and lower limits of the climbing slope of unit n respectively.

[0066] 2) Unit output constraints

[0067]

[0068] Where: They are the upper and lower limits of the output of unit n respectively.

[0069] (2) Power balance constraints

[0070] 1) Electric energy balance

[0071]

[0072] Where: are the photovoltaic and wind power generation in scenario w at time t; P L is the RIES electrical load demand.

[0073] 2) Backup balance

[0074] The reserve capacity provided by IRES in the reserve market is provided by CHP, HFC, ES and curtailable load.

[0075]

[0076] Where: They are the cogeneration unit, hydrogen fuel cell, energy storage system, and spare capacity reserved for load reduction in scenario w at time t.

[0077] (3) Power market quotation constraints

[0078] The market stipulates that both spot electricity and ancillary service transactions should be subject to market quotation limits, that is, RIES's quotations in the electricity and reserve markets should meet the following limit constraints.

[0079]

[0080] Where: is the price quoted by RIES in the electricity and reserve market at time t; are the upper limits of RIES’s bids in the electricity energy and reserve markets at time t respectively.

[0081] (4) Load constraints can be reduced

[0082]

[0083] Where: P CL It is the maximum load reduction of RIES.

[0084] (5) Energy storage constraints

[0085]

[0086] In the formula, are the charging and discharging power of energy storage respectively; is the maximum charging / discharging power of the energy storage system; They are the maximum and minimum energy storage of the energy storage battery respectively; It is the energy storage of the energy storage battery unit in scenario w at time t.

[0087] The gurobi optimization solver is used to solve the transaction decision method of the regional integrated energy system considering the participation in the electric energy-reserve market to obtain the optimal decision.

[0088] Gurobi uses a variety of optimization algorithms, including linear programming (LP), integer programming (IP), quadratic programming (QP), and mixed integer programming (MILP), and automatically selects the appropriate algorithm based on the characteristics of the problem. The specific steps are as follows:

[0089] (1) Define the mathematical model and constraints, and Gurobi automatically performs preprocessing and algorithm selection operations;

[0090] (2) Set solver parameters, including time limit, number of iterations, and accuracy requirements;

[0091] (3) Call the solve() method of the gurobi solver to start the solution process;

[0092] (4) After the solution is completed, obtain the objective function value, the optimal value of the decision variable, and the slack variable information from the gurobi result object.

[0093] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for making trading decisions for a regional integrated energy system that considers participation in an electric energy-reserve market is implemented.

[0094] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the steps of the regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market.

[0095] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0096] The present invention proposes a regional integrated energy system participating in the electric energy-reserve market framework; proposes to use the Latin hypercube method and the rapid previous generation elimination technology to solve the problem of randomness of wind and solar resource output in the regional integrated energy system; introduces a reserve capacity incentive mechanism to construct an objective function that considers maximizing the benefits of the regional integrated energy system participating in the electric energy-reserve market; establishes a regional integrated energy optimal decision-making model that considers participating in the electric energy-reserve market and uses a solver to solve it to obtain the optimal strategy; effectively promotes the absorption of a high proportion of renewable energy and fully dispatches demand-side resources, can provide backup demand for the power grid, greatly alleviates the problem of short-term power system power shortage, and achieves mutual benefit and win-win results between the power grid and the user side. The present invention can reasonably solve the problem of regional power supply and demand tension caused by the short-term social peak climb and the increase in the grid reserve gap, and greatly realizes the regulatory effect of demand-side resources participating in the power market on the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] In order to more clearly illustrate the technical solutions and implementation examples of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0098] Figure 1 It is a flow chart of a transaction decision method for a regional integrated energy system considering participation in an electric energy-reserve market provided by an embodiment of the present invention.

[0099] Figure 2 This is a diagram of the architecture of a regional integrated energy system participating in an electric energy-reserve market provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0100] The present invention is further described below. The following examples are only used to more clearly illustrate the technical solution of the present invention, and are not intended to limit the scope of the present invention.

[0101] Embodiment: The embodiment of the present invention discloses a transaction decision method for a regional integrated energy system considering participation in an electric energy-reserve market, comprising the following steps:

[0102] Step 1: Propose a framework for regional integrated energy systems to participate in the electric energy-reserve market, and introduce its trading model and service mechanism in the electric energy-reserve market, including:

[0103] The regional integrated energy system (RIES) studied includes photovoltaic, wind power, gas boilers, cogeneration devices, hydrogen energy cascade systems, electric heat and gas hydrogen storage devices and various demand-side loads. RIES has its own network structure to manage and dispatch its internal resources, and can also participate in energy transactions in the electricity market as an independent entity. The electric energy market and the reserve market are managed by an independent system operator (ISO). RIES purchases and sells electricity from the electric energy market to earn the difference, and can also provide reserve capacity to the reserve market to make a profit. It is proposed that RIES participate in the electric energy-reserve market architecture, such as Figure 1 shown.

[0104] RIES interacts with the electric energy-reserve market to conduct energy declaration and resource scheduling to obtain corresponding benefits. RIES covers various types of demand-side resources downward, signs reserve contracts with them, provides reserve incentive fees to demand-side resources, and supports and encourages demand-side resources to participate in electricity market services.

[0105] In the power market environment, RIES participates in power market transaction decisions as an independent entity. RIES flexibly declares the purchase / sale of electric power to the electric energy market based on its own energy dispatching situation, and the power generation side reports the quantity without quoting. RIES provides backup to the backup market and declares backup capacity. The backup market issues backup events based on the distribution curve of its historical backup data, and the probability of calling backup is normally distributed. RIES has a probability of failure to provide backup services. The power trading center and the dispatching agency make joint decisions on the electric energy-backup market based on the collected quotation information, the load of the entire system, and the demand for backup services. By coordinating RIES to participate in multiple types of market transactions, the optimal decision is made to maximize the benefits of RIES itself.

[0106] Step 2: Propose the Latin hypercube method and fast previous generation elimination technology to solve the problem of randomness of wind and solar resource output in regional integrated energy systems, including:

[0107] A multi-scenario approach is used to describe the uncertainty of wind and solar resources in RIES. Taking wind power resources as an example, we first assume that wind power follows a normal distribution N(μ,σ 2 ), μ is the expected value of the predicted value, and σ is the percentage of its fluctuation. Then, Latin hypercube sampling is used to generate a large number of wind power output scenarios subject to probability distribution constraints, and the generated random scenarios are reduced using the fast predecessor elimination technology based on probability distance. Finally, the wind power output scenarios with corresponding probabilities after reduction are obtained.

[0108] (1) Latin Hypercube Sampling Scene Generation

[0109] Latin hypercube sampling can more accurately translate the probability distribution characteristics of the sampled data. Assume that there are N random variables K1 K N , where K N The cumulative probability distribution function of is:

[0110] F N =F N (K N ) (1)

[0111] Assuming the sampling size is R, then K N The adoption steps are as follows:

[0112] 1) F N The distribution curve is divided into R intervals, and the range of each interval is the same as 1 / R.

[0113] 2) Extract a number in each interval. The sampling point of the i-th interval is K Ni , its cumulative probability P Ni for:

[0114] P Ni =(1 / R)r i +(i-1) / R (2)

[0115] In the formula, r i is a random number in the interval [0,1].

[0116] 3) P Ni Substitution Function, get the corresponding interval sampling value K Ni

[0117]

[0118] 4) Sampling R times by the above method will generate K N R sample values ​​of .

[0119] 5) Generate an N×R dimensional matrix, randomly sort the rows and generate R random scenes.

[0120] (2) Rapid elimination of previous generation technology scenarios

[0121] The rapid predecessor elimination technique usually generates several scenarios to consider the majority of the occurrence states of uncertain parameters in the uncertainty modeling process and introduces a scenario reduction method to select candidate scenarios from all the scenarios generated in the system. After the Latin hypercube sampling method generates a large-scale resource scenario set S, the rapid predecessor elimination technique based on probability distance is used to reduce the number of large-scale scenarios. The steps of this technical method are as follows:

[0122] 1) Compute the geometric distance between each pair of scenes s and s' in S.

[0123] 2) Select the scene d with the smallest sum of probability distances to the remaining scenes.

[0124] 3) Replace scene d with scene r in S that has the closest geometric distance to scene d, add the probability of scene d to the probability of scene r, eliminate scene d, and form a new wind power resource scene set S'.

[0125] 4) Determine whether the number of remaining scenes meets the requirement; if not, repeat steps 1) to 3); if so, end scene reduction.

[0126] The uncertainty handling of photovoltaic resources can also be described using the above-mentioned multi-scenario generation and reduction method.

[0127] Step 3: Under the framework of step 1, combined with the wind and solar processing in step 2, introduce a reserve capacity incentive mechanism to construct an objective function that considers maximizing the benefits of the regional integrated energy system participating in the electric energy-reserve market, including:

[0128] The main goal is to maximize the benefits of the regional integrated energy system participating in the electric energy-reserve market. Therefore, the objective function RIES benefit G can be expressed as follows:

[0129]

[0130] Where: π w is the standardized probability of the wth scenario occurring, %; is the income obtained by RIES in the electric energy market exchange at time t under the w-th scenario, RMB; is the revenue obtained by RIES in the backup market at time t under the w-th scenario, RMB; is the power generation cost of RIES at time t under the w-th scenario, RMB; Energy storage cost at time t in the wth scenario, yuan; is the cost of demand response for load reduction within RIES at time t under the w-th scenario, RMB; ξ is the reserve capacity incentive.

[0131] (1) Electricity market revenue

[0132] RIES earns revenue by purchasing / selling electricity in the electricity market through its own energy dispatch.

[0133]

[0134] Where: P is the clearing price of the electricity market at time t; t RIES,in , P t RIES,out They are the electricity purchased and sold by RIES in the electricity market.

[0135] (2) Reserve market income

[0136] RIES earns revenue in the reserve market by reserving reserve capacity and actually providing reserve service electricity.

[0137]

[0138] Where: The price of reserved reserve capacity provided by RIES to the reserve market at time t; are the reserve market clearing price at time t and the penalty price for RIES not providing reserve services; is the probability of the system calling the standby at time t, which forms a normal distribution law based on historical data; They are the reserve capacity provided by RIES to the reserve market; is the probability of failure of RIES to provide backup service at time t.

[0139] (3) Reserve capacity incentives

[0140] In order to encourage the enthusiasm of resource dispatch on the demand side, a reserve capacity incentive mechanism is introduced. On the premise of satisfying its own operation, the more reserved reserve capacity RIES declares to the reserve market, the more cost incentive compensation will be given to power generation enterprises, further improving the success probability of RIES actually providing reserve services to the reserve market. The specific ratio will be adjusted according to the power market environment.

[0141]

[0142] Where: C RIES,RC The cost of providing reserved spare capacity for RIES; P RIES,RC The reserved reserve capacity reported by RIES to the reserve market; Q RIES is the RIES capacity.

[0143] (4) RIES power generation cost

[0144] The cost of renewable energy generation is relatively low and is ignored in the objective function of RIES. Distributed power sources in RIES include combined heat and power generation units and hydrogen fuel cell generation in the hydrogen energy cascade system.

[0145]

[0146] Where: is the unit cost coefficient; They are respectively the power generation of the cogeneration unit and the hydrogen fuel cell in scenario w at time t.

[0147] (5) Energy storage costs

[0148] Energy storage costs include the charging and discharging costs of the energy storage system as well as the operation and maintenance costs.

[0149]

[0150] Where: are the energy released and charged by the energy storage system at time t; η ES,ch , η ES,dis They are respectively the charging and discharging efficiency of energy storage; The unit charging and discharging cost of the energy storage system; is the unit operation and maintenance cost of the energy storage system; Q ES is the capacity of the energy storage system.

[0151] (6) Can reduce load costs

[0152] The load can be reduced during peak electricity consumption to alleviate the peak electricity consumption; during low electricity consumption periods, it can increase output and promote the consumption of clean energy.

[0153]

[0154] Where: is the load reduction amount aggregated by RIES in scenario w at time t; a1, a2, a3 are the quadratic, linear and constant term parameters of the load reduction cost function.

[0155] Step 4: Based on step 3, construct corresponding constraints, establish a regional comprehensive energy optimal decision-making model considering participation in the electric energy-reserve market, and use the solver to solve and obtain the optimal strategy, including:

[0156] Considering that RIES is equipped with wind and solar power units, gas boilers, cogeneration units, hydrogen energy cascade utilization systems, energy storage systems and various flexible loads, RIES participates in the energy trading of the electricity-reserve market as an independent entity.

[0157] The constraints for RIES to participate in the power energy-reserve market transaction decision-making include output upper and lower limit constraints, ramp constraints, safety constraints and conventional constraints. The constraints should also include:

[0158] (1) Unit constraints

[0159] 1) Unit climbing constraints

[0160]

[0161] Where: They are the upper and lower limits of the climbing slope of unit n respectively.

[0162] 2) Unit output constraints

[0163]

[0164] Where: They are the upper and lower limits of the output of unit n respectively.

[0165] (2) Power balance constraints

[0166] 1) Electric energy balance

[0167]

[0168] Where: are the photovoltaic and wind power generation in scenario w at time t; P L is the RIES electrical load demand.

[0169] 2) Backup balance

[0170] The reserve capacity provided by IRES in the reserve market is provided by CHP, HFC, ES and curtailable load.

[0171]

[0172] Where: They are the cogeneration unit, hydrogen fuel cell, energy storage system, and spare capacity reserved for load reduction in scenario w at time t.

[0173] (3) Power market quotation constraints

[0174] The market stipulates that both spot electricity and ancillary service transactions should be subject to market quotation limits, that is, RIES's quotations in the electricity and reserve markets should meet the following limit constraints.

[0175]

[0176] Where: is the price quoted by RIES in the electricity and reserve market at time t; are the upper limits of RIES’s bids in the electricity energy and reserve markets at time t respectively.

[0177] (4) Load constraints can be reduced

[0178]

[0179] Where: It is the maximum load reduction of RIES.

[0180] (5) Energy storage constraints

[0181]

[0182] In the formula, are the charging and discharging power of energy storage respectively; is the maximum charging / discharging power of the energy storage system; They are the maximum and minimum energy storage of the energy storage battery respectively; It is the energy storage of the energy storage battery unit in scenario w at time t.

[0183] The gurobi optimization solver is used to solve the transaction decision method of the regional integrated energy system considering the participation in the electric energy-reserve market to obtain the optimal decision.

[0184] Gurobi uses a variety of optimization algorithms, including linear programming (LP), integer programming (IP), quadratic programming (QP), and mixed integer programming (MILP), and automatically selects the appropriate algorithm based on the characteristics of the problem. The specific steps are as follows:

[0185] (1) Define the mathematical model and constraints, and Gurobi automatically performs preprocessing and algorithm selection operations;

[0186] (2) Set solver parameters, including time limit, number of iterations, and accuracy requirements;

[0187] (3) Call the solve() method of the gurobi solver to start the solution process;

[0188] (4) After the solution is completed, obtain the objective function value, the optimal value of the decision variable, and the slack variable information from the gurobi result object.

[0189] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for making trading decisions for a regional integrated energy system that considers participation in an electric energy-reserve market is implemented.

[0190] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the steps of the regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market.

[0191] It should be noted that the above embodiments are not intended to limit the protection scope of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. A transaction decision-making method for a regional integrated energy system considering participation in an electric energy-reserve market, characterized in that: include: Step 1: Propose a framework for regional integrated energy systems to participate in the electric energy-reserve market, and introduce its trading model and service mechanism in the electric energy-reserve market; Step 2: Propose the use of Latin hypercube sampling method and fast previous generation elimination technology to solve the randomness problem of wind and solar resource output in regional integrated energy systems; Step 3: Under the framework of step 1, combined with the uncertainty processing of wind and solar resources in step 2, introduce the reserve capacity incentive mechanism and construct an objective function that considers maximizing the benefits of the regional integrated energy system participating in the electric energy-reserve market; Step 4: Based on step 3, construct corresponding constraints, establish a regional comprehensive energy trading decision model that considers participation in the electric energy-reserve market, and use the solver to solve it to obtain the optimal strategy.

2. The regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market according to claim 1 is characterized in that: The implementation process of step 1 is: The study of the Regional Integrated Energy System (RIES) includes photovoltaic, wind power, gas boilers, cogeneration devices, hydrogen energy cascade systems, electric heat and gas hydrogen storage devices, and various demand-side loads. RIES has its own network structure to manage and dispatch its internal resources. It can also participate in the energy trading of the electricity market as an independent entity. The electric energy market and the reserve market are managed by an independent system operator (ISO). RIES purchases and sells electricity from the electric energy market to earn the difference, and can also provide reserve capacity to the reserve market to make a profit. It is proposed that RIES participates in the electric energy-reserve market architecture. RIES interacts with the power energy-reserve market to report energy and dispatch resources to obtain corresponding benefits. RIES covers various demand-side resources, signs reserve contracts with them, provides reserve incentive fees to demand-side resources, and supports and encourages demand-side resources to participate in power market services. In the power market environment, RIES participates in power market transaction decisions as an independent entity. RIES flexibly declares the purchase / sale of electric power to the electric energy market based on its own energy dispatching situation. The power generation side reports the quantity but does not quote. RIES provides backup to the backup market and declares backup capacity. The backup market issues backup events based on the distribution curve of its historical backup data, and the probability of calling backup is normally distributed. RIES has a probability of failure to provide backup services. The power trading center and the dispatching agency make joint decisions on the electric energy-backup market based on the collected quotation information, the whole system load and the backup service demand. By coordinating RIES to participate in multiple types of market transactions, the optimal decision is made to maximize the benefits of RIES itself.

3. The regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market according to claim 1 is characterized in that: The implementation process of step 2 is: A multi-scenario approach is used to describe the uncertainty of wind and solar resources in RIES. First, it is assumed that wind power follows a normal distribution N(μ,σ 2 ), μ is the expected value of the predicted value, σ is the percentage of its fluctuation, then, Latin hypercube sampling is used to generate a large number of wind power output scenarios subject to probability distribution constraints, and the generated random scenarios are reduced by using the fast predecessor elimination technology based on probability distance. Finally, the wind power output scenarios with corresponding probabilities after reduction are obtained. (1) Latin hypercube sampling scene generation, Latin hypercube sampling can more accurately translate the probability distribution characteristics of the sampled data. Assume that there are N random variables K1 K N , where K N The cumulative probability distribution function of is: F N =F N (K N ) (1) Assuming the sampling size is R, then K N The adoption steps are as follows: 1) F N The distribution curve is divided into R intervals, and the range of each interval is the same as 1 / R. 2) Extract a number in each interval, and the sampling point of the i-th interval is K Ni , its cumulative probability P Ni for: P Ni =(1 / R)r i +(i-1) / R (2) In the formula, r i is a random number in the interval [0,1], 3) P Ni Substitution Function, get the corresponding interval sampling value K Ni 4) Sampling R times by the above method will generate K N R sample values ​​of 5) Generate an N×R dimensional matrix, randomly sort the rows and generate R random scenes. (2) Rapid elimination of previous generation technology scenarios, The steps of this technical method are as follows: 1) Calculate the geometric distance between each pair of scenes s and s' in S, 2) Select the scene d with the smallest sum of probability distances to the remaining scenes, 3) Replace scene d with scene r in S that is closest to scene d in geometric distance, add the probability of scene d to the probability of scene r, eliminate scene d, and form a new wind power resource scene set S'. 4) Determine whether the number of remaining scenes meets the requirement; if not, repeat steps 1) to 3); if so, end scene reduction.

4. The regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market according to claim 1 is characterized in that: The implementation process of step 3 is: The main goal is to maximize the benefits of the regional integrated energy system participating in the electric energy-reserve market. Therefore, the objective function RIES benefit G is expressed as follows: Where: π w is the standardized probability of the wth scenario occurring, %; is the income obtained by RIES in the electric energy market exchange at time t under the w-th scenario, RMB; is the revenue obtained by RIES in the backup market at time t under the w-th scenario, RMB; is the power generation cost of RIES at time t under the w-th scenario, RMB; Energy storage cost at time t in the wth scenario, yuan; is the cost of demand response for load reduction within RIES at time t under the w-th scenario, RMB; ξ is the reserve capacity incentive, (1) Electricity market revenue, RIES makes profits by purchasing / selling electricity in the electricity market through its own energy dispatch. Where: P is the clearing price of the electricity market at time t; t RIES,in , P t RIES,out are the electricity purchased and sold by RIES in the electricity market, (2) Reserve market income RIES earns revenue in the reserve market by reserving reserve capacity and actually providing reserve service electricity. Where: The price of reserved reserve capacity provided by RIES to the reserve market at time t; are the reserve market clearing price at time t and the penalty price for RIES not providing reserve services; is the probability of the system calling the standby at time t, which forms a normal distribution law based on historical data; They are the reserve capacity provided by RIES to the reserve market; is the probability of failure of RIES to provide backup service at time t, (3) Reserve capacity incentives, In order to encourage the enthusiasm of demand-side resource scheduling, a reserve capacity incentive mechanism is introduced. On the premise of meeting its own operation, the more reserved reserve capacity RIES reports to the reserve market, the more cost incentive compensation will be given to power generation companies by 30%-50%, further improving the success probability of RIES actually providing reserve services to the reserve market. The specific ratio will be adjusted according to the power market environment. Where: C RIES,RC The cost of providing reserved spare capacity for RIES; P RIES,RC The reserved reserve capacity reported by RIES to the reserve market; Q RIES is the RIES capacity, (4) RIES power generation cost The cost of renewable energy generation is relatively low and is ignored in the objective function of RIES. Distributed power sources in RIES include combined heat and power generation units and hydrogen fuel cell generation in the hydrogen energy cascade system. Where: is the unit cost coefficient; They are the power generation of the cogeneration unit and the hydrogen fuel cell in the scenario w at time t, (5) Energy storage costs Energy storage costs include the energy storage system charging and discharging costs and operation and maintenance costs. Where: are the energy released and charged by the energy storage system at time t; η ES,ch , η ES,dis They are the charging and discharging efficiency of energy storage respectively; The unit charging and discharging cost of the energy storage system; is the unit operation and maintenance cost of the energy storage system; Q ES is the capacity of the energy storage system, (6) Can reduce load costs The load can be reduced during peak hours to alleviate peak electricity consumption; during low electricity consumption hours, it can increase output and promote the consumption of clean energy. Where: is the load reduction amount aggregated by RIES in scenario w at time t; a1, a2, a3 are the quadratic, linear and constant term parameters of the load reduction cost function.

5. The regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market according to claim 1 is characterized in that: The implementation process of step 4 is: Considering that RIES is equipped with wind and solar power units, gas boilers, cogeneration units, hydrogen energy cascade utilization systems, energy storage systems and various flexible loads, RIES participates in the energy trading of the power-reserve market as an independent entity. The constraints for RIES to participate in the power energy-reserve market transaction decision-making include output upper and lower limit constraints, ramp constraints, safety constraints and conventional constraints. The constraints should also include: (1) Unit constraints 1) Unit climbing constraints Where: are the upper and lower limits of the ramp rate of unit n, 2) Unit output constraints Where: are the upper and lower limits of the output of unit n, (2) Power balance constraints 1) Electric energy balance Where: are the photovoltaic and wind power generation in scenario w at time t; P L is the RIES electrical load demand, 2) Backup balance The reserve capacity provided by IRES in the reserve market is provided by CHP, HFC, ES and curtailable load. Where: They are the cogeneration unit, hydrogen fuel cell, energy storage system, and spare capacity reserved for load reduction in scenario w at time t. (3) Power market quotation constraints The market stipulates that spot electricity and ancillary service transactions should all be subject to market quotation limits, that is, RIES's quotation in the electricity and reserve markets should meet the following limit constraints: Where: is the price quoted by RIES in the electricity and reserve market at time t; are the upper limits of RIES’s bids in the energy and reserve markets at time t, (4) Load constraints can be reduced Where: is the maximum load reduction of RIES, (5) Energy storage constraints In the formula, are the charging and discharging power of energy storage respectively; is the maximum charging / discharging power of the energy storage system; They are the maximum and minimum energy storage of the energy storage battery respectively; is the energy storage of the energy storage battery unit in scenario w at time t, The gurobi optimization solver is used to solve the transaction decision method of the regional integrated energy system considering the participation in the electric energy-reserve market, and the optimal strategy is obtained. Gurobi uses a variety of optimization algorithms, including linear programming (LP), integer programming (IP), quadratic programming (QP), and mixed integer programming (MILP), and automatically selects the appropriate algorithm based on the characteristics of the problem. The specific steps are as follows: (1) Define the mathematical model and constraints, and Gurobi automatically performs preprocessing and algorithm selection operations; (2) Set solver parameters, including time limit, number of iterations, and accuracy requirements; (3) Call the solve() method of the gurobi solver to start the solution process; (4) After the solution is completed, obtain the objective function value, the optimal value of the decision variable, and the slack variable information from the gurobi result object.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market as described in any one of claims 1 to 5 above.

7. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, the steps of the regional integrated energy system transaction decision-making method considering participation in the electric energy-reserve market as described in any one of claims 1-5 are implemented.

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