Multi-energy system optimization method and device considering multiple time scales

Abstract

The multi-energy system optimization method and device under multiple time scales are considered. The optimization goal is to minimize the total cost of the multi-energy system in the day-ahead stage, and the upper limit of the load reduction amount of each user participating in the demand response is determined; the optimization goal is to minimize the total cost of the multi-energy system in the intraday stage, and under the constraint of the upper limit of the load reduction amount of each user participating in the demand response, the actual value of the load reduction amount of each user participating in the demand response is determined; the actual value of the load reduction amount of each user participating in the demand response and the electricity price of each time period in the intraday stage are used to update the electricity quantity and electricity price elasticity matrix of each user; according to the change in electricity price in the real-time stage, the real-time value of the load reduction amount of each user participating in the demand response is determined using the updated electricity quantity and electricity price elasticity matrix of each user; based on the real-time value of the load reduction amount of each user participating in the demand response, the multi-energy system decision model is solved to achieve the optimization of the output of each unit and the demand response subsidy price in the multi-energy system under multiple time scales.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-energy system operation control, and specifically relates to a multi-energy system optimization method and device considering multiple time scales. Background Art

[0002] The multi-energy system takes the power system as its core and couples various energy subsystems such as heat, cooling, and natural gas. During the planning, construction, and operation process, it organically coordinates and optimizes the operation of each link of "source, grid, load, and storage" from a physical level, thus forming an integrated production, supply, and consumption system.

[0003] In existing technologies, the understanding of multi-energy systems is more limited to cogeneration, and their operation optimization is also limited to improving energy efficiency and ensuring the supply of thermal power. In addition, their energy composition is relatively simple, mainly thermal power, and the corresponding thermal power supply model is more typical in northern my country. With the implementation of the dual carbon strategy, the proportion of renewable energy in multi-energy systems is increasing, which not only makes the supply model of multi-energy systems more diverse and complex, but also brings problems such as wind and solar power consumption and supply fluctuations.

[0004] Existing technologies for optimizing the operation of multi-energy systems often focus solely on the supply side, without considering the role of the load side. This makes it difficult to achieve coordinated and optimized operation across all aspects of the "source, grid, load, and storage" ecosystem. In reality, the presence of a large number of flexible loads on the load side represents a vast potential resource for the operation and scheduling of multi-energy systems. Furthermore, the addition of intelligent devices such as new energy vehicles and distributed energy storage provides a wider range of solutions. When flexible loads are coupled with clear rules and economic benefits, demand response emerges. Summary of the Invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a multi-energy system optimization method and device taking into account multiple time scales. Based on the load reduction amount of each user participating in demand response at different time scales, the total cost of the multi-energy system at each time scale is optimized, thereby achieving the optimization of the output of each unit in the multi-energy system at multiple time scales and the demand response subsidy price.

[0006] The present invention adopts the following technical solutions.

[0007] The present invention proposes a multi-energy system optimization method considering multiple time scales, including the day-ahead stage, the intraday stage, and the real-time stage, including:

[0008] Obtain the change in electricity prices in each period, and use the user's electricity and price elasticity matrix to determine the load reduction amount of users participating in demand response in each period;

[0009] Taking the minimization of the total cost of the multi-energy system in the day-ahead phase as the optimization objective, the upper limit of the load reduction amount of each user participating in demand response is determined;

[0010] Taking the minimization of the total cost of the multi-energy system during the day as the optimization goal, the actual value of each user's load reduction in demand response is determined under the constraint of the upper limit of the load reduction amount of each user participating in demand response. The actual value of each user's load reduction in demand response and the electricity price of each time period during the day are used to update the electricity price elasticity matrix of each user.

[0011] According to the change in electricity prices in the real-time stage, the real-time value of the load reduction amount of each user participating in demand response is determined using the updated electricity quantity and electricity price elasticity matrix of each user; a multi-energy system decision model is constructed by minimizing the total cost of the multi-energy system in the day-ahead stage, the total cost of the multi-energy system in the intraday stage, and the total cost of the multi-energy system in the real-time stage. Based on the real-time value of the load reduction amount of each user participating in demand response, the multi-energy system decision model is iteratively solved to determine the output of traditional energy units in the multi-energy system, the output of new energy units, and the subsidy price of users participating in demand response.

[0012] Preferably, the load reduction ratio model of the i-th user participating in demand response in time period t satisfies the following relationship:

[0013]

[0014] Where ΔL i,t is the load reduction of the i-th user participating in demand response in period t, L i,t is the load of the i-th user in time period t, ΔL i,t / L i,t is the load reduction ratio of the i-th user participating in demand response in time period t, M i is the electricity price elasticity matrix of the i-th user, Δρ t is the change in electricity price during period t, ρ t is the electricity price in period t, Δρ t / ρ t is the electricity price change ratio in time period t, t = 1, 2, ..., T, T is the total number of time periods, i = 1, 2, ..., N, N is the total number of users participating in demand response;

[0015] Among them, the electricity price elasticity matrix of the i-th user satisfies the following relationship:

[0016]

[0017] In the formula, the diagonal elements δ of the electricity price elasticity matrix are i,11 ,……,δ i,TTis the real-time electricity price elasticity coefficient of the i-th user in each time period, and the elements on the non-diagonal line of the electricity quantity and price elasticity matrix are the inter-time electricity price elasticity coefficients of the i-th user in each time period.

[0018] Preferably, the optimization objective is to minimize the total cost of the multi-energy system in the day-ahead phase, and determine the upper limit of the load reduction amount of each user participating in demand response, including:

[0019] The total cost of the multi-energy system in the day-ahead phase includes: energy supply cost, operation and maintenance cost, start-up and shutdown cost, wind and solar curtailment cost, carbon emission quota trading cost, green certificate trading cost and demand response expenditure cost;

[0020] Taking the minimization of the total cost of the multi-energy system in the day-ahead stage as the objective function, under the power constraints and ramping constraints of the thermal power units, the power balance constraints of the multi-energy system, and the constraints of the energy storage battery, the load reduction amount of each user participating in demand response corresponding to the minimum total cost of the multi-energy system in the day-ahead stage is obtained iteratively as the upper limit of the load reduction amount of each user participating in demand response.

[0021] Preferably, the upper limit of the load reduction amount of each user participating in demand response satisfies the following relationship:

[0022]

[0023] Where ΔL i,t is the load reduction of the i-th user participating in demand response in time period t, is the upper limit of the load reduction of the i-th user participating in demand response in time period t, ξ i,t is the load reduction ratio of the i-th user participating in demand response in time period t, ξ i,t =ΔL i,t / L i,t , is the initial load value of the i-th user participating in demand response in time period t, and T is the total number of time periods.

[0024] Preferably, the total cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0025]

[0026] Where, is the total cost of the multi-energy system in the day-ahead phase, is the energy supply cost of the multi-energy system in the day-ahead phase, is the operation and maintenance cost of the multi-energy system in the day-ahead phase, is the start-up and shutdown cost of the multi-energy system in the day-ahead phase, is the cost of wind and solar curtailment in the multi-energy system during the day-ahead period, The demand response cost of the multi-energy system in the day-ahead phase is is the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase, It is the green certificate transaction cost of the multi-energy system in the day-ahead phase.

[0027] Preferably, the energy supply cost of the multi-energy system in the day-ahead stage satisfies the following relationship:

[0028]

[0029] Where, is the energy supply cost of the multi-energy system in the day-ahead phase, is the amount of electricity purchased from the grid by the multi-energy system in period t, ρ t is the real-time electricity price in period t, and are the energy supply costs of thermal power and gas turbine in time period t, respectively, and T is the total number of time periods.

[0030] Preferably, the start-up and shutdown costs of the multi-energy system in the day-ahead phase satisfy the following relationship:

[0031]

[0032] Where, is the start-up and shutdown cost of the multi-energy system in the day-ahead phase, and are virtual variables representing the start and stop status of thermal power and gas turbine in period t. When thermal power and gas turbine are in the start state in period t, and is 1, otherwise it is 0. is the single cost of starting a thermal power unit, is the single cost of shutting down a thermal power unit, is the cost per gas turbine startup, is the single cost of shutting down the gas turbine, and T is the total number of time periods.

[0033] Preferably, the wind and solar curtailment costs of the multi-energy system in the day-ahead phase satisfy the following relationship:

[0034]

[0035] Where, is the cost of wind and solar curtailment in the multi-energy system during the day-ahead period, and are respectively the wind power output forecast value and photovoltaic output forecast value of the multi-energy system in time period t during the day-ahead period, and are the actual wind power output and photovoltaic power output of the multi-energy system in time period t during the day-ahead period, η WT and ηWT are the prices of wind power and photovoltaic power purchased by the multi-energy system, respectively, and T is the total number of time periods.

[0036] Preferably, the demand response expenditure cost of the multi-energy system in the day-ahead stage satisfies the following relationship:

[0037]

[0038] Where, is the demand response cost of the multi-energy system in the day-ahead phase, N I is the total number of users who participated in the incentive-based demand response in the day-ahead phase, is the unit price of subsidy paid by the multi-energy system to the i-th user participating in the incentive-based demand response in time period t during the day-ahead phase, is the load reduction of the i-th user participating in the incentive-based demand response in time period t during the day-ahead phase, and T is the total number of time periods.

[0039] Preferably, the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0040]

[0041] Where, is the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase, ρ ct is the price of unit carbon emission rights in the carbon trading market, CE IES is the actual carbon emissions of the multi-energy system, Q IES It is the carbon emission quota of the multi-energy system, and CED is the carbon emission offset by new energy.

[0042] Preferably, the green certificate transaction cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0043]

[0044] Where, is the green certificate transaction cost of the multi-energy system in the day-ahead phase, Q gecq The net quota of green certificates held for multi-energy systems, The unit price of selling excess green certificates for multi-energy systems in the market, The unit price of green certificates for multi-energy systems, The unit price for the penalty imposed when the actual green certificates of a multi-energy system do not meet the quota requirements.

[0045] Preferably, the total cost of the multi-energy system in the daily stage satisfies the following relationship:

[0046]

[0047] Where, is the total cost of the multi-energy system during the day, is the energy supply cost of the multi-energy system during the day, is the operation and maintenance cost of the multi-energy system during the daily period, is the start-up and shutdown cost of the multi-energy system during the day, is the cost of wind and solar curtailment in the multi-energy system during the day, The demand response cost of the multi-energy system during the day, is the carbon emission quota transaction cost of the multi-energy system in the intraday stage, The transaction cost of green certificates for multi-energy systems during the intraday period;

[0048] Among them, the intraday stage Respectively with the previous stage same.

[0049] Preferably, the demand response expenditure cost of the multi-energy system during the day satisfies the following relationship:

[0050]

[0051] Where, is the demand response cost of the multi-energy system during the day, N II is the total number of users participating in the incentive demand response during the day, is the unit price of subsidy paid by the multi-energy system to the i-th user participating in the incentive-based demand response in time period t during the intraday stage, is the actual value of the load reduction of the i-th user participating in the incentive-based demand response in time period t during the intraday stage, and T is the total number of time periods.

[0052] Preferably, the objective function is to minimize the total cost of the multi-energy system during the intra-day period. Under the power constraints and ramping constraints of the thermal power units, the power balance constraints of the multi-energy system, and the constraints of the energy storage battery, an iterative solution is performed to obtain the actual value of the load reduction amount of each user participating in the demand response corresponding to the minimum total cost of the multi-energy system during the intra-day period.

[0053] Using the actual value of the load reduction amount of each user participating in demand response and the electricity price of each period during the day, the electricity price elasticity matrix of each user is updated according to the following relationship:

[0054]

[0055] Where, is the actual value of the load reduction of the i-th user who participates in the incentive demand response in time period t during the intraday stage, L i,t is the load of the i-th user in time period t, is the actual load reduction ratio of the i-th user participating in demand response in time period t during the intraday stage, M′ i is the updated electricity price elasticity matrix of the i-th user, is the change in electricity price during period t within the day, ρ t is the electricity price in period t, is the actual change ratio of the electricity price in time period t during the day, t=1,2,…,T, T is the total number of time periods, i=1,2,…,N II , N II is the total number of users who participated in the incentive-based demand response during the day.

[0056] Preferably, the real-time value of the load reduction amount of each user participating in demand response satisfies the following relationship:

[0057]

[0058] Where, is the real-time value of the load reduction of the i-th user participating in the incentive-based demand response in time period t in the real-time stage, L i,t is the load of the i-th user in time period t, is the real-time load reduction ratio of the i-th user participating in demand response in time period t, M′ i is the updated electricity price elasticity matrix of the i-th user, is the change in electricity price in time period t in the real-time stage, ρ t is the electricity price in period t, is the real-time change ratio of the electricity price in time period t in the real-time stage, t=1,2,…,T, T is the total number of time periods, i=1,2,…,N III , N III The total number of users participating in incentive-based demand in the real-time stage.

[0059] Preferably, the total cost of the multi-energy system in the real-time stage satisfies the following relationship:

[0060]

[0061] Where, is the total cost of the multi-energy system in the real-time stage, is the energy supply cost of the multi-energy system in the real-time stage, The operation and maintenance cost of the multi-energy system in the real-time stage, is the start-up and shutdown cost of the multi-energy system in the real-time stage, is the wind and solar curtailment cost of the multi-energy system in real time, Cost of demand response for multi-energy systems in real-time phase, is the carbon emission quota transaction cost of the multi-energy system in the real-time stage, The green certificate transaction cost of the multi-energy system in the real-time stage is The output deviation loss cost of the multi-energy system in the real-time stage;

[0062] Among them, the real-time stage Respectively with the previous stage same.

[0063] Preferably, the energy supply cost of the multi-energy system in the real-time stage and the output deviation loss cost of the multi-energy system in the real-time stage satisfy the following relationship:

[0064]

[0065] Where, is the energy supply cost of the multi-energy system in the real-time stage, ρ t is the electricity price in period t, is the amount of electricity purchased from the grid by the multi-energy system in time period t in the real-time stage, In the real-time stage, the multi-energy system needs to purchase more power from the grid due to insufficient output in time period t, P t GT is the power of the gas turbine in time period t in the real-time stage, is the power added by the gas turbine in time period t during the real-time phase, λ gas is the unit price of gas, ρ gas is the electricity price corresponding to the gas turbine, P t TP is the power of the thermal power unit in time period t in the real-time stage, is the power added by the thermal power unit in time period t in the real-time stage, The power of the thermal power unit in time period t in the real-time stage is The energy cost at is the output deviation loss cost of the multi-energy system in the real-time stage, ζ GT ,ζ TP ,ζ ES are the penalty coefficients for gas turbines, thermal power, and energy storage, respectively. are the energy storage charging power and discharging power adjusted due to output deviation in period t in the real-time stage, t0 is the starting time of the deviation in period t, and τ is the time duration for the multi-energy system to adjust the output.

[0066] Preferably, the demand response expenditure cost of the multi-energy system in the real-time stage satisfies the following relationship:

[0067]

[0068] Where, is the demand response cost of the multi-energy system in the real-time phase, N III The total number of users participating in the incentive demand in the real-time stage, is the unit price of subsidy paid by the multi-energy system to the i-th user participating in the incentive-based demand response in time period t in the real-time stage, is the real-time value of load reduction of the i-th user participating in the incentive-based demand response in time period t in the real-time stage, and T is the total number of time periods.

[0069] Preferably, the real-time value of the load reduction amount of each user participating in the demand response is used as the state variable, and the output of the traditional energy units, the output of the new energy units and the subsidy prices of the users participating in the demand response in the multi-energy system are used as decision variables to establish the state transfer equation of the multi-energy system; the decision model of the multi-energy system and the state transfer equation are jointly solved iteratively to obtain the output of the traditional energy units, the output of the new energy units and the subsidy prices of the users participating in the demand response as the optimization result of the multi-energy system.

[0070] The present invention also proposes a multi-energy system optimization device considering multiple time scales, wherein the multiple time scales include a day-ahead stage, an intraday stage, and a real-time stage, including:

[0071] The module for calculating the real-time value of load reduction is used to obtain the change in electricity price in each time period, and to determine the load reduction of users participating in demand response in each time period by using the user's electricity and electricity price elasticity matrix; to determine the upper limit of the load reduction of each user participating in demand response by taking the minimum total cost of the multi-energy system in the day-ahead stage as the optimization goal; to determine the actual value of the load reduction of each user participating in demand response under the constraint of the upper limit of the load reduction of each user participating in demand response by taking the minimum total cost of the multi-energy system in the intraday stage as the optimization goal; to update the electricity and electricity price elasticity matrix of each user by using the actual value of the load reduction of each user participating in demand response and the electricity price of each time period in the intraday stage; and to determine the real-time value of the load reduction of each user participating in demand response by using the updated electricity and electricity price elasticity matrix of each user according to the change in electricity price in the real-time stage;

[0072] The multi-energy system optimization module is used to construct a multi-energy system decision model by minimizing the total cost of the multi-energy system in the day-ahead stage, the intraday stage, and the real-time stage. Based on the real-time value of the load reduction amount of each user participating in demand response, the multi-energy system decision model is iteratively solved to determine the output of traditional energy units in the multi-energy system, the output of new energy units, and the subsidy price of users participating in demand response.

[0073] A terminal includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0074] A computer-readable storage medium stores a computer program thereon, which implements the steps of the method when executed by a processor.

[0075] The beneficial effects of the present invention are that, compared with the prior art, it at least includes optimizing the output of each unit in the multi-energy system and the demand response subsidy price at multiple time scales, which can more precisely match the supply and demand of different energy sources in different time periods. The multi-energy system decision-making model proposed in the present invention not only takes into account the energy efficiency improvement of the multi-energy system, but also takes into account the consumption of new energy and the interaction between sources and loads. It dynamically optimizes multiple objectives of the multi-energy system operation at multiple time scales, can enhance the development and utilization of potential resources on the load side of the multi-energy system, and reflects the role of market mechanisms in the decision-making process. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a flow chart of the multi-energy system optimization method considering multiple time scales proposed in the present invention. DETAILED DESCRIPTION

[0077] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0078] The present invention provides a multi-energy system optimization method considering multiple time scales, wherein the multiple time scales include a day-ahead stage, an intraday stage, and a real-time stage. In the embodiment, 15 minutes is used as an intraday stage, and 5 minutes is used as a real-time stage.

[0079] like Figure 1 As shown in Figure 2, the multi-energy system optimization method includes:

[0080] Step 1: Obtain the electricity price change in each time period, and use the user's electricity price elasticity matrix to determine the load reduction amount of users participating in demand response in each time period.

[0081] The load reduction of the i-th user participating in demand response in time period t satisfies the following relationship:

[0082]

[0083] Where ΔL i,t is the load reduction of the i-th user participating in demand response in period t, L i,t is the load of the i-th user in time period t, ΔLi,t / L i,t is the load reduction ratio of the i-th user participating in demand response in time period t, M i is the electricity price elasticity matrix of the i-th user, Δρ t is the change in electricity price during period t, ρ t is the electricity price in period t, Δρ t / ρ t is the electricity price change ratio in time period t, t = 1, 2, ..., T, T is the total number of time periods, i = 1, 2, ..., N, N is the total number of users participating in demand response;

[0084] Among them, the electricity price elasticity matrix of the i-th user satisfies the following relationship:

[0085]

[0086] In the formula, the diagonal elements δ of the electricity price elasticity matrix are i,11 ,……,δ i,TT is the real-time electricity price elasticity coefficient of the i-th user in each time period, and the elements on the non-diagonal line of the electricity quantity and price elasticity matrix are the inter-time electricity price elasticity coefficients of the i-th user in each time period.

[0087] Step 2: Taking the minimization of the total cost of the multi-energy system in the day-ahead phase as the optimization objective, determine the upper limit of the load reduction amount of each user participating in demand response.

[0088] Specifically, step 2 includes:

[0089] Step 2.1: The total cost of the multi-energy system in the day-ahead phase includes: energy supply cost, operation and maintenance cost, start-up and shutdown cost, wind and solar curtailment cost, carbon emission quota trading cost, green certificate trading cost, and demand response expenditure cost;

[0090] The multi-energy system includes, but is not limited to, thermal power, gas turbines, wind power, and photovoltaic units on the supply side. Thermal power and gas turbines participate in power supply and thermal-to-electricity conversion, while wind power and photovoltaic units only participate in power supply. The output of wind power and photovoltaic units is purchased by the multi-energy system at an agreed price to promote the consumption of new energy. The multi-energy system does not bear the operation and maintenance costs of wind and solar units. The supply side is equipped with energy storage equipment, mainly energy storage batteries. To avoid supply gaps, the multi-energy system will purchase electricity from the power grid on the day before to maintain the thermal-to-electricity balance of the system. The start and stop status of the thermal-to-electricity conversion equipment is determined by the multi-energy system according to the operation plan on the day before and will not be changed during the day.

[0091] Based on the above situation, without considering the time period, the total cost of multi-energy system operation throughout the day includes:

[0092] ① Energy supply cost: including the cost of purchasing electricity from the power grid by the multi-energy system and the cost of thermal power and gas turbine energy supply;

[0093] ②Operation and maintenance costs: including the operation and maintenance costs of thermoelectric conversion equipment and energy storage batteries;

[0094] ③ Start-up and shutdown costs: startup and shutdown costs of thermoelectric conversion equipment;

[0095] ④ Cost of curtailed wind and solar power: Losses caused by the predicted wind power and photovoltaic output of the multi-energy system exceeding the real-time absorption power;

[0096] ⑤ Demand response compensation cost: the economic expenditure of the multi-energy system to compensate users who participate in demand response.

[0097] ⑥Transaction costs: carbon trading and green certificate transaction costs.

[0098] The total cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0099]

[0100] Where, is the total cost of the multi-energy system in the day-ahead phase, is the energy supply cost of the multi-energy system in the day-ahead phase, is the operation and maintenance cost of the multi-energy system in the day-ahead phase, is the start-up and shutdown cost of the multi-energy system in the day-ahead phase, is the cost of wind and solar curtailment in the multi-energy system during the day-ahead period, The demand response cost of the multi-energy system in the day-ahead phase is is the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase, The green certificate transaction cost of the multi-energy system in the day-ahead phase;

[0101] Specifically, the energy supply cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0102]

[0103] Where, is the energy supply cost of the multi-energy system in the day-ahead phase, is the amount of electricity purchased from the grid by the multi-energy system in period t, ρ t is the real-time electricity price in period t, and are the energy supply costs of thermal power and gas turbine in time period t, respectively, and T is the total number of time periods;

[0104] Among them, the energy supply costs of thermal power and gas turbines in time period t satisfy the following relationship:

[0105]

[0106] In the formula, α, β, and c are coefficients in the quadratic cost function of thermal power. is the output of the thermal power unit in period t, ρ coal is the price of thermal coal, is the output of the gas turbine at time period t, ρ gas is the price of natural gas, λ gas is the energy conversion coefficient of natural gas.

[0107] Specifically, the operation and maintenance cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0108]

[0109] Where, is the operation and maintenance cost of the multi-energy system in the day-ahead phase, η TP ,η GT ,η ES are the operation and maintenance cost coefficients of thermal power, gas turbines and energy storage batteries, and are the charging power and discharging power of the energy storage battery in time period t, Δt is the charging and discharging time, and T is the total number of time periods;

[0110] Specifically, the start-up and shutdown costs of the multi-energy system in the day-ahead phase satisfy the following relationship:

[0111]

[0112] Where, is the start-up and shutdown cost of the multi-energy system in the day-ahead phase, and are virtual variables representing the start and stop status of thermal power and gas turbine in period t. When thermal power and gas turbine are in the start state in period t, and is 1, otherwise it is 0. is the single cost of starting a thermal power unit, is the single cost of shutting down a thermal power unit, is the cost per gas turbine startup, is the single cost of shutting down the gas turbine, T is the total number of time periods;

[0113] Among them, the cost of wind and solar curtailment in the multi-energy system during the day-ahead phase satisfies the following relationship:

[0114]

[0115] Where, is the cost of wind and solar curtailment in the multi-energy system during the day-ahead period, and are respectively the wind power output forecast value and photovoltaic output forecast value of the multi-energy system in time period t during the day-ahead period, and are the actual wind power output and photovoltaic power output of the multi-energy system in time period t during the day-ahead period, η WT and η WT are the prices for purchasing wind power and photovoltaic power for the multi-energy system, respectively, and T is the total number of time periods;

[0116] In the day-ahead phase, there are price-based demand response and incentive-based demand response. The multi-energy system only needs to pay actual economic compensation to users of incentive-based demand response. Therefore, the demand response expenditure cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0117]

[0118] Where, is the demand response cost of the multi-energy system in the day-ahead phase, N I is the total number of users who participated in the incentive-based demand response in the day-ahead phase, is the unit price of subsidy paid by the multi-energy system to the i-th user participating in the incentive-based demand response in time period t during the day-ahead phase, is the load reduction of the i-th user who participated in the incentive-based demand response in time period t during the day-ahead phase, where T is the total number of time periods;

[0119] The carbon emission quota transaction cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0120]

[0121] Where, is the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase, ρ ct is the price of unit carbon emission rights in the carbon trading market, CE IES is the actual carbon emissions of the multi-energy system, Q IES is the carbon emission quota of the multi-energy system, and CED is the carbon emission offset by new energy;

[0122] The green certificate transaction cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0123]

[0124] Where, is the green certificate transaction cost of the multi-energy system in the day-ahead phase, Q gecq The net quota of green certificates held for multi-energy systems, The unit price of selling excess green certificates for multi-energy systems in the market, The unit price of green certificates for multi-energy systems, The unit price for the penalty imposed when the actual green certificates of a multi-energy system do not meet the quota requirements.

[0125] Step 2.2: Taking the minimization of the total cost of the multi-energy system during the day-ahead phase as the objective function, under the power and ramping constraints of the thermal power units, the power balance constraints of the multi-energy system, and the constraints of the energy storage battery, iteratively solve the load reduction amount for each user participating in demand response that corresponds to the minimum total cost of the multi-energy system during the day-ahead phase. This amount is used as the upper limit of the load reduction amount for each user participating in demand response.

[0126] Specifically, the upper limit of the load reduction amount of each user participating in demand response satisfies the following relationship:

[0127]

[0128] Where ΔL i,t is the load reduction of the i-th user participating in demand response in time period t, is the upper limit of the load reduction of the i-th user participating in demand response in time period t, ξ i,t is the load reduction ratio of the i-th user participating in demand response in time period t, ξ i，t =ΔL i，t / L i，t , is the initial load value of the i-th user participating in demand response in time period t, and T is the total number of time periods;

[0129] Specifically, the constraints of the thermal power unit include power constraints and ramp constraints, which satisfy the following relationship:

[0130]

[0131] Where, and are the maximum operating powers of thermal power and gas turbine respectively; and are the lower and upper limits of the ramp power of thermal power units respectively; and are the lower and upper limits of the gas turbine climbing power respectively.

[0132] The power balance constraints on the supply side and the load side satisfy the following relationship:

[0133]

[0134] Where, L t is the total load at the load end of the multi-energy system in time period t.

[0135] The constraints of the energy storage battery satisfy the following relationship:

[0136]

[0137] Where Q rated is the energy storage battery power, SOC min and SOC max are the lower and upper limits of the state of charge, P rated is the maximum charging power, κ ch (t) and κ dis (t) are virtual variables of the charge and discharge state of the energy storage battery in period t, and the charging time of the energy storage battery in period t is κ ch (t) is 1, κ dis (t) is 0, the discharge time of the energy storage battery in time period t is κ ch (t) is 0, κ dis (t) is 1.

[0138] The above formulas respectively indicate that the capacity constraint, charge and discharge power constraint, charge and discharge state constraint, initial state and final state of charge of ESS are the same.

[0139] In actual application, the load reduction amount of users participating in demand response in each time period determined in step 1 is the user load reduction amount under price-based demand response, which is different from the user load reduction amount under incentive-based demand response. The total cost of the multi-energy system in the day-ahead stage is calculated based on the output forecast values ​​of various energy units. Therefore, the load reduction amount of each user participating in demand response corresponding to the minimum total cost of the multi-energy system in the day-ahead stage is obtained by iterative solution, which is used as the upper limit of the load reduction amount of each user participating in demand response, thereby realizing the introduction of the user load reduction amount under price-based demand response into incentive-based demand response.

[0140] Step 3: Taking the minimization of the total cost of the multi-energy system in the intraday stage as the optimization goal, determine the actual value of the load reduction amount of each user participating in the demand response under the constraint of the upper limit of the load reduction amount of each user participating in the demand response; using the actual value of the load reduction amount of each user participating in the demand response and the electricity price of each time period in the intraday stage, update the electricity quantity and electricity price elasticity matrix of each user.

[0141] Specifically, step 3 includes:

[0142] In step 3.1, the total cost of the multi-energy system in the day-ahead phase satisfies the following relationship:

[0143]

[0144] Where, is the total cost of the multi-energy system during the day, is the energy supply cost of the multi-energy system during the day, is the operation and maintenance cost of the multi-energy system during the daily period, is the start-up and shutdown cost of the multi-energy system during the day, is the cost of wind and solar curtailment in the multi-energy system during the day, The demand response cost of the multi-energy system during the day, is the carbon emission quota transaction cost of the multi-energy system in the intraday stage, It is the green certificate transaction cost of multi-energy system in the intraday stage.

[0145] Among them, the intraday stage Respectively with the previous stage same;

[0146] The demand response expenditure cost of the multi-energy system during the day satisfies the following relationship:

[0147]

[0148] Where, is the demand response cost of the multi-energy system during the day, N II is the total number of users participating in the incentive demand response during the day, is the unit price of subsidy paid by the multi-energy system to the i-th user participating in the incentive-based demand response in time period t during the intraday stage, is the actual value of the load reduction of the i-th user participating in the incentive-based demand response in time period t during the intraday stage, and T is the total number of time periods.

[0149] In step 3.2, taking the minimization of the total cost of the multi-energy system in the intra-day phase as the objective function, under the power constraints and ramping constraints of the thermal power units, the power balance constraints of the multi-energy system, and the constraints of the energy storage battery, the actual value of the load reduction amount of each user participating in the demand response corresponding to the minimum total cost of the multi-energy system in the intra-day phase is obtained by iterative solution.

[0150] The present invention proposes to take the minimization of the total cost of the multi-energy system in the intraday stage as the optimization goal, and determine the actual value of the load reduction amount of each user participating in the demand response under the constraint of the upper limit of the load reduction amount of each user participating in the demand response, taking into account the uncertainty of the new energy output in the intraday stage and the volatility of the user load. Under the constraint of the upper limit of the load reduction amount of each user participating in the demand response, the users whose reduction amount exceeds the constraint are eliminated, and the operating cost of the intraday stage is further reduced on the basis of the total cost of the multi-energy system in the day-ahead stage. At the same time, it is beneficial to improve the absorption rate of new energy, thereby reducing the cost of wind and solar power abandonment.

[0151] Step 3.3: Update the electricity price elasticity matrix of each user using the actual load reduction amount of each user participating in demand response and the electricity price of each period during the day;

[0152] Update the electricity price elasticity matrix of each user using the following relationship:

[0153]

[0154] Where, is the actual value of the load reduction of the i-th user who participates in the incentive demand response in time period t during the intraday stage, L i,t is the load of the i-th user in time period t, is the actual load reduction ratio of the i-th user participating in demand response in time period t during the intraday stage, M i ′ is the updated electricity price elasticity matrix of the i-th user, is the change in electricity price during period t within the day, ρ t is the electricity price in period t, is the actual change ratio of the electricity price in time period t during the day, t=1,2,…,T, T is the total number of time periods, i=1,2,…,N II , N II is the total number of users who participated in the incentive-based demand response during the day.

[0155] Step 4: Based on the change in electricity prices in the real-time stage, the updated electricity and electricity price elasticity matrix of each user is used to determine the real-time value of the load reduction amount of each user participating in the demand response; the multi-energy system decision model is constructed by minimizing the total cost of the multi-energy system in the day-ahead stage, the total cost of the multi-energy system in the intraday stage, and the total cost of the multi-energy system in the real-time stage. Based on the real-time value of the load reduction amount of each user participating in the demand response, the multi-energy system decision model is iteratively solved to determine the output of traditional energy units in the multi-energy system, the output of new energy units, and the subsidy price of users participating in the demand response.

[0156] Specifically, step 4 includes:

[0157] In step 4.1, the real-time value of the load reduction amount of each user participating in demand response satisfies the following relationship:

[0158]

[0159] Where, is the real-time value of the load reduction of the i-th user participating in the incentive-based demand response in time period t in the real-time stage, L i,t is the load of the i-th user in time period t, is the real-time load reduction ratio of the i-th user participating in demand response in time period t, M i ′ is the updated electricity price elasticity matrix of the i-th user, is the change in electricity price in time period t in the real-time stage, ρ t is the electricity price in period t, is the real-time change ratio of the electricity price in time period t in the real-time stage, t=1,2,…,T, T is the total number of time periods, i=1,2,…,N III , N III The total number of users participating in incentive-based demand in the real-time stage.

[0160] In step 4.2, the total cost of the multi-energy system in the real-time stage satisfies the following relationship:

[0161]

[0162] Where, is the total cost of the multi-energy system in the real-time stage, is the energy supply cost of the multi-energy system in the real-time stage, The operation and maintenance cost of the multi-energy system in the real-time stage, is the start-up and shutdown cost of the multi-energy system in the real-time stage, is the wind and solar curtailment cost of the multi-energy system in real time, Cost of demand response for multi-energy systems in real-time phase, is the carbon emission quota transaction cost of the multi-energy system in the real-time stage, The green certificate transaction cost of the multi-energy system in the real-time stage is is the output deviation loss cost of the multi-energy system in the real-time stage.

[0163] In this embodiment, the real-time stage is at the 5-minute level. The multi-energy system will adjust its output plan based on the real-time source and load data. When there is a deviation between the planned output and the real-time output, according to market rules, the multi-energy system will bear the output deviation loss cost;

[0164] Among them, the real-time stage Respectively with the previous stage same.

[0165] When output deviation occurs in the real-time phase, if there is a supply gap, the multi-energy system will purchase electricity from the grid and increase the output of thermal power units, which will increase the consumption of thermal coal and natural gas. When there is an oversupply, the user end of the multi-energy system cannot absorb it in a short time, resulting in waste. Assuming the starting point of the deviation in time period t is t0, and the time for the multi-energy system to adjust the output is τ, then the energy supply cost of the multi-energy system in the real-time phase and the output deviation loss cost of the multi-energy system in the real-time phase satisfy the following relationship:

[0166]

[0167] Where, is the energy supply cost of the multi-energy system in the real-time stage, ρ t is the electricity price in period t, is the amount of electricity purchased from the grid by the multi-energy system in time period t in the real-time stage, In the real-time stage, the multi-energy system needs to purchase more power from the grid due to insufficient output in time period t, P t GT is the power of the gas turbine in time period t in the real-time stage, is the power added by the gas turbine in time period t during the real-time phase, λ gas is the unit price of gas, ρ gas is the electricity price corresponding to the gas turbine, P t TP is the power of the thermal power unit in time period t in the real-time stage, is the power added by the thermal power unit in time period t in the real-time stage, The power of the thermal power unit in time period t in the real-time stage is The energy cost at is the output deviation loss cost of the multi-energy system in the real-time stage, ζ GT ,ζ TP ,ζ ES are the penalty coefficients for gas turbines, thermal power, and energy storage, respectively. are the energy storage charging power and discharging power adjusted due to output deviation in period t in the real-time stage, t0 is the starting time of the deviation in period t, and τ is the duration of output adjustment of the multi-energy system;

[0168] The demand response expenditure cost of the multi-energy system in the real-time stage satisfies the following relationship:

[0169]

[0170] Where, is the demand response cost of the multi-energy system in the real-time phase, N III The total number of users participating in the incentive demand in the real-time stage, is the unit price of subsidy paid by the multi-energy system to the i-th user participating in the incentive-based demand response in time period t in the real-time stage, is the real-time value of load reduction of the i-th user participating in the incentive-based demand response in time period t in the real-time stage, and T is the total number of time periods.

[0171] In step 4.3, the multi-energy system decision model is constructed by minimizing the total cost of the multi-energy system in the day-ahead phase, the intraday phase, and the real-time phase, satisfying the following relationship:

[0172]

[0173] Where F is the multi-energy system decision model

[0174] Step 4.4: Using the real-time load reduction of each user participating in demand response as the state variable, the output of traditional energy units in the multi-energy system, the output of new energy units, and the subsidy price of users participating in demand response as the decision variables, establish the state transition equation of the multi-energy system;

[0175] Introducing the state transition equation, the state variables of the system at time t+1 are determined by the state variables and decision variables at the previous time. In the optimization problem of multi-energy systems, the state variables are the demand changes at the load end, and the decision variables are the unit output and the price of the subsidy demand response. Therefore, the state transition equation of the multi-energy system satisfies the following relationship:

[0176] Γ t+1 =f(Γ t , χ t , P j，t ,λ k，t )

[0177] Where, Γ t is the state of the multi-energy system in time period t, f() is the state transfer function, χ t is the real-time value of the load reduction amount of each user participating in the demand response of the multi-energy system in time period t, P j，t is the output of the jth unit in the multi-energy system in time period t, λ ktt is the subsidy price of the multi-energy system to the kth user participating in demand response in time period t.

[0178] In step 4.5, the multi-energy system decision model and state transition equation are jointly solved iteratively to obtain the output of traditional energy units, the output of new energy units, and the subsidy price of users participating in demand response as the multi-energy system optimization result.

[0179] The specific solution method is to solve the Bellman equation by Python, where the probability distribution of the state variable is given by χ t The historical data is determined through statistical analysis.

[0180] The multi-energy system decision-making model proposed in the present invention not only takes into account the energy efficiency improvement of the multi-energy system, but also takes into account the new energy consumption and source-load interaction. It dynamically optimizes multiple objectives of the multi-energy system operation at multiple time scales, and can enhance the development and utilization of potential resources on the load side of the multi-energy system. In the decision-making process, it reflects the role of the market mechanism.

[0181] The present invention also proposes a multi-energy system optimization device considering multiple time scales, wherein the multiple time scales include a day-ahead stage, an intraday stage, and a real-time stage, including:

[0182] The module for calculating the real-time value of load reduction is used to obtain the change in electricity price in each time period, and to determine the load reduction of users participating in demand response in each time period by using the user's electricity and electricity price elasticity matrix; to determine the upper limit of the load reduction of each user participating in demand response by taking the minimum total cost of the multi-energy system in the day-ahead stage as the optimization goal; to determine the actual value of the load reduction of each user participating in demand response under the constraint of the upper limit of the load reduction of each user participating in demand response by taking the minimum total cost of the multi-energy system in the intraday stage as the optimization goal; to update the electricity and electricity price elasticity matrix of each user by using the actual value of the load reduction of each user participating in demand response and the electricity price of each time period in the intraday stage; and to determine the real-time value of the load reduction of each user participating in demand response by using the updated electricity and electricity price elasticity matrix of each user according to the change in electricity price in the real-time stage;

[0183] The multi-energy system optimization module is used to construct a multi-energy system decision model by minimizing the total cost of the multi-energy system in the day-ahead stage, the intraday stage, and the real-time stage. Based on the real-time value of the load reduction amount of each user participating in demand response, the multi-energy system decision model is iteratively solved to determine the output of traditional energy units in the multi-energy system, the output of new energy units, and the subsidy price of users participating in demand response.

[0184] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0185] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0186] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0187] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A multi-energy system optimization method considering multiple time scales, characterized in that: include: Obtain the change in electricity prices in each period, and use the user's electricity and price elasticity matrix to determine the load reduction amount of users participating in demand response in each period; The total cost of the multi-energy system in the day-ahead phase includes: energy supply cost, operation and maintenance cost, start-up and shutdown cost, wind and solar curtailment cost, carbon emission quota trading cost, green certificate trading cost, and demand response expenditure cost. Taking the minimization of the total cost of the multi-energy system in the day-ahead phase as the optimization goal, under the power constraints and ramping constraints of the thermal power units, the power balance constraints of the multi-energy system, and the constraints of the energy storage battery, the load reduction amount of each user participating in the demand response corresponding to the minimum total cost of the multi-energy system in the day-ahead phase is iteratively solved as the upper limit of the load reduction amount of each user participating in the demand response. Taking the minimization of the total cost of the multi-energy system during the day as the optimization goal, the actual value of each user's load reduction in demand response is determined under the constraint of the upper limit of the load reduction amount of each user participating in demand response. The actual value of each user's load reduction in demand response and the electricity price of each time period during the day are used to update the electricity price elasticity matrix of each user. Based on the change in electricity prices in the real-time phase, the updated electricity and price elasticity matrix of each user is used to determine the real-time value of the load reduction amount of each user participating in demand response; a multi-energy system decision model is constructed by minimizing the total cost of the multi-energy system in the day-ahead phase, the intraday phase, and the real-time phase. Based on the real-time value of the load reduction amount of each user participating in demand response, the multi-energy system decision model is iteratively solved to determine the output of traditional energy units in the multi-energy system, the output of new energy units, and the subsidy price for users participating in demand response; Taking the real-time value of the load reduction amount of each user participating in demand response as the state variable, and the output of traditional energy units, the output of new energy units and the subsidy price of users participating in demand response in the multi-energy system as the decision variables, the state transition equation of the multi-energy system is established; the multi-energy system decision model and the state transition equation are jointly solved iteratively to obtain the output of traditional energy units, the output of new energy units and the subsidy price of users participating in demand response as the optimization result of the multi-energy system.

2. The multi-energy system optimization method considering multiple time scales according to claim 1 is characterized in that: Participating in demand response Users in the time period The load reduction ratio model satisfies the following relationship: Where, For the first Users in the time period The load reduction, For the Users in the time period The load, For the first Users in the time period The load reduction ratio, For the The electricity price elasticity matrix of each user is: For the period The change in electricity price, For the period The electricity price, For the period The electricity price change ratio, , is the total number of time periods, , is the total number of users participating in demand response; Among them, The electricity price elasticity matrix of each user satisfies the following relationship: In the formula, the elements on the diagonal of the electricity price elasticity matrix are 、……、 For the The real-time electricity price elasticity coefficient of each user in each period, the elements on the non-diagonal line of the electricity price elasticity matrix are The inter-period electricity price elasticity coefficient of each user in each period.

3. The multi-energy system optimization method considering multiple time scales according to claim 1 is characterized in that: The upper limit of the load reduction amount of each user participating in demand response satisfies the following relationship: Where, For the first Users in the time period The load reduction, For the first Users in the time period The upper limit of load reduction, For the first Users in the time period The load reduction ratio, , For the first Users in the time period The initial load value, The total number of time periods.

4. The multi-energy system optimization method considering multiple time scales according to claim 1 is characterized in that: The total cost of the multi-energy system in the day-ahead phase satisfies the following relationship: Where, is the total cost of the multi-energy system in the day-ahead phase, is the energy supply cost of the multi-energy system in the day-ahead phase, is the operation and maintenance cost of the multi-energy system in the day-ahead phase, is the start-up and shutdown cost of the multi-energy system in the day-ahead phase, is the cost of wind and solar curtailment in the multi-energy system during the day-ahead period, The demand response cost of the multi-energy system in the day-ahead phase is is the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase, It is the green certificate transaction cost of the multi-energy system in the day-ahead phase.

5. The multi-energy system optimization method considering multiple time scales according to claim 4 is characterized in that: The energy supply cost of the multi-energy system in the day-ahead phase satisfies the following relationship: Where, is the energy supply cost of the multi-energy system in the day-ahead phase, For multi-energy systems in time periods The amount of electricity purchased from the grid, For the period Real-time electricity prices, and Thermal power and gas turbine in the period The energy cost, The total number of time periods.

6. The multi-energy system optimization method considering multiple time scales according to claim 4 is characterized in that: The start-up and shutdown costs of the multi-energy system in the day-ahead phase satisfy the following relationship: Where, is the start-up and shutdown cost of the multi-energy system in the day-ahead phase, and Thermal power and gas turbine in the period The virtual variable of the start and stop status, in the period When thermal power and gas turbine are in startup state, and is 1, otherwise it is 0. is the single cost of starting a thermal power unit, is the single cost of shutting down a thermal power unit, is the cost per gas turbine startup, is the cost per shutdown of the gas turbine, The total number of time periods.

7. The multi-energy system optimization method considering multiple time scales according to claim 4 is characterized in that: The cost of wind and solar curtailment in the multi-energy system during the day-ahead phase satisfies the following relationship: Where, is the cost of wind and solar curtailment in the multi-energy system during the day-ahead period, and They are the multi-energy system in the day-ahead phase and the time period The wind power output forecast value and photovoltaic output forecast value, and They are the multi-energy system in the day-ahead phase and the time period The actual output of wind power and photovoltaic power, and The prices for purchasing wind power and photovoltaic power for the multi-energy system are respectively: The total number of time periods.

8. The multi-energy system optimization method considering multiple time scales according to claim 4 is characterized in that: The demand response expenditure cost of the multi-energy system in the day-ahead phase satisfies the following relationship: Where, The demand response cost of the multi-energy system in the day-ahead phase is is the total number of users who participated in the incentive-based demand response in the day-ahead phase, The multi-energy system in the day-ahead phase Towards the first The unit price of subsidy for each user, For the period during the day before Participate in incentive-based demand response The load reduction of each user, The total number of time periods.

9. The multi-energy system optimization method considering multiple time scales according to claim 4 is characterized in that: The carbon emission quota transaction cost of the multi-energy system in the day-ahead phase satisfies the following relationship: Where, is the carbon emission quota transaction cost of the multi-energy system in the day-ahead phase, is the price of unit carbon emission rights in the carbon trading market, is the actual carbon emissions of the multi-energy system, Carbon emission quotas for multi-energy systems, Carbon emissions offset by new energy.

10. The multi-energy system optimization method considering multiple time scales according to claim 4, characterized in that: The green certificate transaction cost of the multi-energy system in the day-ahead phase satisfies the following relationship: Where, is the green certificate transaction cost of the multi-energy system in the day-ahead phase, The net quota of green certificates held for multi-energy systems, The unit price of selling excess green certificates for multi-energy systems in the market, The unit price of green certificates for multi-energy systems, The unit price for the penalty imposed when the actual green certificates of a multi-energy system do not meet the quota requirements.

11. The multi-energy system optimization method considering multiple time scales according to claim 4, characterized in that: The total cost of the multi-energy system in the daily stage satisfies the following relationship: Where, is the total cost of the multi-energy system during the day, is the energy supply cost of the multi-energy system during the day, is the operation and maintenance cost of the multi-energy system during the daily period, is the start-up and shutdown cost of the multi-energy system during the day, is the cost of wind and solar curtailment in the multi-energy system during the day, The demand response cost of the multi-energy system during the day, is the carbon emission quota transaction cost of the multi-energy system in the intraday stage, The transaction cost of green certificates for multi-energy systems during the intraday period; Among them, the intraday stage 、 、 、 Respectively with the previous stage 、 、 、 same.

12. The multi-energy system optimization method considering multiple time scales according to claim 11, characterized in that: The demand response expenditure cost of the multi-energy system during the day satisfies the following relationship: Where, The demand response cost of the multi-energy system during the day, is the total number of users participating in the incentive demand response during the day, For the multi-energy system in the intraday stage Towards the first The unit price of subsidy for each user, For the intraday period Participate in incentive-based demand response The actual value of the load reduction amount of each user, The total number of time periods.

13. The multi-energy system optimization method considering multiple time scales according to claim 12, characterized in that: Taking the minimization of the total cost of the multi-energy system during the daily phase as the objective function, under the power and ramping constraints of the thermal power units, the power balance constraints of the multi-energy system, and the constraints of the energy storage battery, an iterative solution is used to obtain the actual value of the load reduction amount of each user participating in demand response corresponding to the minimum total cost of the multi-energy system during the daily phase. Using the actual value of the load reduction amount of each user participating in demand response and the electricity price of each period during the day, the electricity price elasticity matrix of each user is updated according to the following relationship: Where, For the intraday period Participate in incentive-based demand response The actual value of the load reduction amount of each user, For the Users in the time period The load, The first Users in the time period The actual load reduction ratio, After the update The electricity price elasticity matrix of each user is: Intraday period The change in electricity price, For the period The electricity price, Intraday period The actual change ratio of electricity price is , is the total number of time periods, , is the total number of users who participated in the incentive-based demand response during the day.

14. The multi-energy system optimization method considering multiple time scales according to claim 13, characterized in that: The real-time value of the load reduction amount of each user participating in demand response satisfies the following relationship: Where, For the real-time phase in the period Participate in incentive-based demand response The real-time value of the load reduction amount of each user, For the Users in the time period The load, For the first Users in the time period The real-time load reduction ratio, After the update The electricity price elasticity matrix of each user is: For the real-time phase The change in electricity price, For the period The electricity price, For the real-time phase The real-time change ratio of electricity prices, , is the total number of time periods, , The total number of users participating in incentive-based demand in the real-time stage.

15. The multi-energy system optimization method considering multiple time scales according to claim 11, characterized in that: The total cost of the multi-energy system in the real-time stage satisfies the following relationship: Where, is the total cost of the multi-energy system in the real-time stage, is the energy supply cost of the multi-energy system in the real-time stage, The operation and maintenance cost of the multi-energy system in the real-time stage, is the start-up and shutdown cost of the multi-energy system in real time, is the wind and solar curtailment cost of the multi-energy system in real time, Cost of demand response for multi-energy systems in real-time phase, is the carbon emission quota transaction cost of the multi-energy system in the real-time stage, The green certificate transaction cost of the multi-energy system in the real-time stage is The output deviation loss cost of the multi-energy system in the real-time stage; Among them, the real-time stage 、 、 、 Respectively with the previous stage 、 、 、 same.

16. The multi-energy system optimization method considering multiple time scales according to claim 15, characterized in that: The energy supply cost of the multi-energy system in the real-time stage and the output deviation loss cost of the multi-energy system in the real-time stage satisfy the following relationship: Where, is the energy supply cost of the multi-energy system in the real-time stage, For the period The electricity price, For the multi-energy system in the real-time stage The amount of electricity purchased from the grid, For the multi-energy system in the real-time stage Because of insufficient output, it is necessary to purchase more power from the grid. For the real-time phase in the period The power of the gas turbine, For the real-time phase in the period The increased power of the gas turbine, is the unit price of gas, is the electricity price corresponding to the gas turbine, For the real-time phase in the period The power of thermal power units, For the real-time phase in the period The increased power of thermal power units, For the real-time phase in the period The power of the thermal power unit is The energy cost at is the output deviation loss cost of the multi-energy system in the real-time stage, 、 、 are the penalty coefficients for gas turbines, thermal power, and energy storage, respectively. 、 In the real-time phase, Because the output deviation adjusts the energy storage charging power and discharging power, For the period The starting point of the deviation, Adjust output duration for multi-energy systems.

17. The multi-energy system optimization method considering multiple time scales according to claim 15, characterized in that: The demand response expenditure cost of the multi-energy system in the real-time stage satisfies the following relationship: Where, Cost of demand response for multi-energy systems in real-time phase, The total number of users participating in the incentive demand in the real-time stage, For the multi-energy system in the real-time stage Towards the first The unit price of subsidy for each user, For the real-time phase in the period Participate in incentive-based demand response The real-time value of load reduction for each user, The total number of time periods.

18. A multi-energy system optimization device considering multiple time scales, wherein the multiple time scales include day-ahead stage, intraday stage and real-time stage, characterized in that: include: The real-time load reduction calculation module is used to obtain the electricity price changes in each time period and use the user's electricity price elasticity matrix to determine the load reduction amount of users participating in demand response in each time period; The total cost of the multi-energy system in the day-ahead stage includes: energy supply cost, operation and maintenance cost, start-up and shutdown cost, wind and solar curtailment cost, carbon emission quota trading cost, green certificate trading cost and demand response expenditure cost; taking the minimization of the total cost of the multi-energy system in the day-ahead stage as the optimization goal, under the power constraint and ramp constraint of the thermal power unit, the power balance constraint of the multi-energy system and the constraint of the energy storage battery, the load reduction amount of each user participating in the demand response corresponding to the minimum total cost of the multi-energy system in the day-ahead stage is iteratively solved as the upper limit of the load reduction amount of each user participating in the demand response; taking the minimization of the total cost of the multi-energy system in the intraday stage as the optimization goal, under the constraint of the upper limit of the load reduction amount of each user participating in the demand response, the actual value of the load reduction amount of each user participating in the demand response is determined; using the actual value of the load reduction amount of each user participating in the demand response and the electricity price of each time period in the intraday stage, the electricity quantity and electricity price elasticity matrix of each user is updated; according to the change in electricity price in the real-time stage, the real-time value of the load reduction amount of each user participating in the demand response is determined using the updated electricity quantity and electricity price elasticity matrix of each user; The multi-energy system optimization module is used to construct a multi-energy system decision model by minimizing the total cost of the multi-energy system in the day-ahead stage, the intraday stage and the real-time stage. Based on the real-time value of the load reduction amount of each user participating in the demand response, the multi-energy system decision model is iteratively solved to determine the output of traditional energy units, the output of new energy units and the subsidy price of users participating in the demand response in the multi-energy system; the real-time value of the load reduction amount of each user participating in the demand response is used as the state variable, and the output of traditional energy units, the output of new energy units and the subsidy price of users participating in the demand response in the multi-energy system are used as the decision variables to establish the state transition equation of the multi-energy system; the multi-energy system decision model and the state transition equation are jointly iteratively solved to obtain the output of traditional energy units, the output of new energy units and the subsidy price of users participating in the demand response as the multi-energy system optimization result.

19. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 17.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 17 are implemented.

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