Multi-temporal and spatial scale optimization scheduling method for micro-energy grid based on demand response

Abstract

The present invention discloses a multi-temporal and spatial scale optimization scheduling method for a micro-energy network based on demand response, and relates to the field of integrated energy smart grid. The scheduling method models and analyzes each device in the micro-energy network from the perspective of energy, and constructs a multi-micro-energy network integrated energy system model. Then, a multi-temporal and spatial scale operation model of a collaborative optimization framework consisting of three layers, upper, middle and lower, is established: the upper layer is the day-ahead scheduling stage, which schedules the entire integrated energy system; the middle layer is the intraday scheduling stage, which optimizes each micro-energy network; the lower layer is the real-time scheduling stage, which adjusts the unbalanced power in each micro-energy network; the three stages respectively introduce replaceable, transferable and reducible loads, and step-by-step participate in demand response optimization scheduling. Compared with the mainstream scheduling strategy, the present invention can further correct the multi-energy load curve, promote the balance of energy supply and demand, achieve system economy and reliability operation, is suitable for energy management with multiple communities, and is easier to expand.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy smart grid, and specifically to a multi-temporal and spatial scale optimization scheduling method for a micro energy grid based on demand response. Background Art

[0002] With the gradual depletion of global fossil energy and the intensification of the contradiction between energy supply and demand, efficient energy utilization has become a focus of research in the energy field. In order to achieve the "dual carbon goals", comprehensive energy with the characteristics of multi-energy complementarity and energy class utilization has become an important solution for low-carbon transformation. Micro-energy network, as an important manifestation of comprehensive energy system, can realize the complementary utilization and optimal matching of multi-dimensional energy. With the diversity of urban development, the differences in equipment coupling and energy conversion characteristics between different types of micro-energy networks, and the different response characteristics of various types of equipment have brought great difficulties to the system scheduling and operation. Therefore, one of the research focuses is to comprehensively consider the interests between various micro-energy networks, optimize the scheduling of the adjustable load of micro-energy networks, improve the level of interconnection and mutual assistance of system energy supply, and achieve overall optimization of energy utilization. Summary of the invention

[0003] In order to solve the problems caused by differences in device coupling and energy conversion characteristics between different types of micro-energy networks and different response characteristics of various types of equipment to system scheduling and operation, the present invention provides a multi-temporal and spatial scale optimization scheduling method for micro-energy networks based on demand response.

[0004] The present invention is realized through the following technical solutions: a multi-temporal and spatial scale optimization scheduling method for micro-energy grids based on demand response, which models and analyzes each device in the micro-energy grid from the energy perspective, and constructs a multi-micro-energy grid integrated energy system model. Then, a multi-temporal and spatial scale operation model of a collaborative optimization framework consisting of three layers, upper, middle, and lower, is established: the upper layer is the day-ahead scheduling stage, which schedules the entire integrated energy system; the middle layer is the intraday scheduling stage, which optimizes each micro-energy grid; the lower layer is the real-time scheduling stage, which adjusts the unbalanced power in each micro-energy grid; the three stages respectively introduce replaceable, transferable, and reducible loads, and step-by-step participate in demand response optimization scheduling, which specifically includes the following steps:

[0005] 1) Model and analyze each device in the micro-energy grid from the energy perspective, and build a multi-micro-energy grid comprehensive energy system model:

[0006] ① CHP unit mathematical model: CHP unit refers to the combined heat and power unit;

[0007]

[0008]

[0009] In the formula, H chp Indicates the output thermal power of the CHP unit; Indicates the thermal-electric conversion efficiency of the CHP unit, which is 90%; P chp Indicates the power generation of the CHP unit; G chp Indicates the amount of natural gas consumed by the CHP unit; β chp Indicates the power generation efficiency of the CHP unit, which is 90%; H ng Indicates the lower calorific value of natural gas, taking 9.7kWh / m 3 ;H chp,i,max Indicates the maximum value of heat generated by the CHP unit;

[0010] ②Mathematical model of electric boiler:

[0011] H eb =η eb ·P eb

[0012]

[0013] In the formula, H eb Indicates the output thermal power of the electric boiler; β eb Indicates the heating efficiency of the electric boiler, which is 95%; P eb Indicates the electric power of the electric boiler; H eb,i,max Indicates the maximum value of the electric boiler power;

[0014] ③Mathematical model of water electrolysis device:

[0015]

[0016]

[0017]

[0018] In the formula, A ec Indicates the amount of hydrogen produced by the water electrolysis device; β ec Indicates the efficiency of the electrolysis device, the value is 90%; V ec Indicates the rated capacity of the electrolytic device; A H2 The fixed hydrogen production is represented by 350; P ecin Indicates the power consumption of the electrolysis device; P ec Indicates the rated power of the electrolysis device; a 0 、b 0 、c 0 is the efficiency function coefficient, with values ​​of 0.0109, 1.0676, and -0.0795 respectively; A ec,i,max Indicates the maximum power of hydrogen produced by electrolysis of water;

[0019] ④ Mathematical model of alkylation unit:

[0020]

[0021]

[0022] In the formula, G ch Indicates the amount of natural gas converted; β ch Indicates hydrogen conversion efficiency, the value is 73%; A ecch Indicates the amount of hydrogen required to produce natural gas; It represents the molar mass conversion coefficient of hydrogen into natural gas; κ represents the mass per m of natural gas pipeline. 3 Corresponding gas mass; G ch,i,max It indicates the maximum power of natural gas obtained after alkylation reaction;

[0023] ⑤Mathematical model of energy storage equipment:

[0024]

[0025] In the formula, x represents the type of energy storage, which are electric energy storage, thermal energy storage, natural gas energy storage and hydrogen energy storage; Respectively represent the energy state before and after energy charging and discharging; η x,c , η x,d and δ x They represent charging efficiency, discharging efficiency and energy loss coefficient respectively; Respectively represent the charging and discharging power at time t;

[0026] ⑥Alternative load mathematical model:

[0027] Taking the replaceable load of electricity / natural gas as an example, the micro-energy network can use gas boilers or electric boilers to supply heat load. In order to facilitate research, the marginal substitution rate of electricity / gas energy is proposed, which represents the ratio of the user's electricity consumption to the natural gas energy consumption in the same time period:

[0028]

[0029] P pl (t) = P buy0 (t)+β eg G rep (t)-P rep (t)

[0030] G pl (t) = G buy0 (t)+P rep (t) / β eg -G rep (t)

[0031] In the formula, ΔP rep (t), ΔG rep(t) respectively represent the changes in the demand for electricity and natural gas before and after the alternative load response; P buy0 (t), G buy0 (t) represent the initial electricity purchase and natural gas purchase of the system respectively; P rep (t), G rep (t) respectively represent the replaceable electricity load and natural gas load participating in the dispatch at time t;

[0032] ⑦Transferable load mathematical model:

[0033]

[0034]

[0035]

[0036]

[0037] Where P extr (t), H extr (t), G extr (t), A extr (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load before transfer; ΔP trans (t), ΔH trans (t), ΔG trans (t), ΔA trans (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load transferred by the user at time t; α t',t =1 means the load is transferred from time t' to time t, α t',t =-1 means the load changes from time t to time t';

[0038] ⑧Mathematical model of load reduction:

[0039] P xj (t) = P exxj (t)-α t ΔP xj (t)

[0040] H xj (t) = H exxj (t)-α t ΔH xj (t)

[0041] G xj (t) = G exxj (t)-α t ΔG xj (t)

[0042] A xj(t) = A exxj (t)-α t ΔA xj (t)

[0043] Where P exxj (t), H exxj (t), G exxj (t), A exxj (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load of the user before the response at time t; ΔP xj (t), ΔH xj (t), ΔG xj (t), ΔA xj (t) respectively represent the reduced electric load, thermal load, natural gas load and hydrogen load at time t; α t =1 means the load is reduced at time t, α t =0 means that the load is not reduced at time t;

[0044] 2) Establish a multi-temporal and spatial scale operation model of a collaborative optimization framework consisting of three layers: upper, middle and lower:

[0045] ①Upper layer:

[0046] a. Objective function:

[0047] The time scale of the day-ahead dispatch phase is 1 hour, and the operating cost C of the entire multi-microgrid integrated energy system is u Minimum is the goal, and the operating cost includes the energy purchase cost Environmental protection costs Equipment maintenance costs Alternative demand response costs Energy interaction cost between micro-energy grids

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] In the formula, They represent electricity price and natural gas price respectively; T represents the dispatch time, which is 24h; They represent the amount of electricity and natural gas purchased by micro-energy grid i from the distribution network at time t, where i = 1 represents industrial micro-energy grid, i = 2 represents commercial micro-energy grid, and i = 3 represents residential micro-energy grid; α represents the unit CO 2 The processing cost is 0.031; β e , β g They represent the carbon emission coefficients of the power grid and natural gas grid in the micro-energy grid, and their values ​​are 0.972 and 0.23 respectively; R represents the equipment that needs to be maintained in the micro-energy grid i; P om,r , They represent the maintenance price and output power of unit r respectively; represent capacity compensation price and energy compensation price respectively; They represent the reserved response part and the actual response part of the load that can be reduced respectively; They represent the sales prices of electricity, heat and natural gas at time t respectively; They represent the interaction energy between micro-energy networks i and j respectively;

[0055] b. Constraints:

[0056] I. Energy purchase upper and lower limits:

[0057]

[0058] Where P b,i,max , G b,i,max They represent the upper limit of the purchase of electricity and gas by the micro-energy network; P b,max , G b,max They represent the total upper limits of electricity and gas in the multi-micro-energy grid integrated energy system respectively;

[0059] II. Energy Interaction Constraints:

[0060]

[0061] Where P ij,max , H ij,max , G ij,max They represent the upper limits of the interaction of electricity, heat and natural gas energy between micro-energy grids i and j respectively; Respectively represent the direction of interaction of electricity, heat, and natural gas energy. A positive value indicates that energy flows from micro-energy grid i to j, and a negative value indicates that energy flows from micro-energy grid j to i.

[0062] III. Energy storage equipment operation constraints:

[0063]

[0064] In the formula, E x,i,min 、E x,i,max Respectively represent the upper and lower limits of the energy storage device; Px,c,i,max , P x,d,i,max Respectively represent the maximum value of charge and discharge energy; x,c,i , x,d,i Respectively represent the 0-1 state quantity of charging and discharging energy;

[0065] IV. Alternative load restraints:

[0066]

[0067] Where P rep,t,i,max , H rep,t,i,max , G rep,t,i,max , A rep,t,i,max They represent the maximum replacement amount of replaceable electricity, heat, natural gas and hydrogen load at time t respectively;

[0068] V. Energy balance constraints:

[0069]

[0070] In the formula, denote the predicted power of distributed generation and load respectively; The load change matrix representing the demand response; The variable matrices representing energy input, energy production, energy conversion, energy storage and energy interaction respectively; C in , C de , C tr , C s , C oc They represent the corresponding coupling matrices respectively;

[0071] ②Middle level:

[0072] a. Objective function:

[0073] The time scale of the intraday optimization scheduling stage is 15 minutes, which is consistent with the objective function of the day-ahead stage. Minimum is the objective function, and the cost includes the energy purchase cost Environmental protection costs Equipment maintenance costs Shiftable Demand Response Costs and energy interaction costs

[0074]

[0075]

[0076] b. Constraints:

[0077] In the intraday stage, the replaceable load adjusted in the day-ahead stage is taken as the known load, and on this basis, intraday optimization scheduling is performed, and the transferable load actively responds to demand, and the other constraints are the same;

[0078] Transferable loads can flexibly choose the load usage time according to the peak and valley periods of the micro-energy grid. Through time-of-use electricity prices and corresponding government incentives, the price changes of loads in different periods are guided, and users can adjust their energy consumption behavior accordingly.

[0079]

[0080] Where P trans,t,i,max , H trans,t,i,max , G trans,t,i,max , A trans,t,i,max They represent the maximum values ​​of electricity, heat, natural gas and hydrogen loads transferred at time t respectively;

[0081] ③Lower layer:

[0082] a. Objective function:

[0083] The time scale of the real-time optimization scheduling stage is 5 minutes, with real-time operation cost The goal is to minimize the imbalance between energy supply and demand; the cost includes the normal operating costs and adjustment costs Among them, this stage is to optimize the scheduling within the micro-energy grid. Energy interaction can be used as the power source of the micro-energy grid to provide energy for it, and it can also be used as a load to consume energy. Therefore, the loss cost of energy interaction will be considered;

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] In the formula, represents the heat price; t s Indicates the start time of intraday optimization; N T represents the total number of optimization cycles within a day; γ e , γ h , γ g They represent the transmission loss coefficients of electricity, heat and natural gas, which are 6%, 8% and 3% respectively; μ p , Respectively represent the adjusted electricity price and the adjusted electricity purchase amount; μ g, Respectively represent the adjusted natural gas price and gas purchase volume; V represents the set in the real-time scheduling stage in micro-energy grid i; μ υ , They represent the adjusted price and adjusted power of the equipment υ respectively; Indicates that the demand response cost can be reduced; Δt is 5 min here;

[0090] b. Constraints:

[0091] In the real-time stage, the replaceable load after day-ahead optimization and the transferable load after intraday optimization are taken as known loads, and the load that can be reduced participates in demand response, and the other constraints are the same;

[0092] All types of loads that can be reduced should meet the following constraints:

[0093]

[0094] Where P xj,t,i,max , H xj,t,i,max , G xj,t,i,max , A xj,t,i,max They respectively represent the maximum values ​​of electricity, heat, natural gas and hydrogen loads that are reduced at time t.

[0095] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a multi-time and space scale optimization scheduling method for micro-energy grid based on demand response, the upper layer performs day-ahead scheduling on the entire multi-micro-energy grid system, the middle layer performs intra-day scheduling on each micro-energy grid, and the lower layer performs real-time adjustment on the unbalanced power in the grid. The three stages respectively introduce replaceable loads, transferable loads and reducible loads to implement a step-by-step demand response strategy, which can further correct the multi-energy load curve, promote the balance of energy supply and demand, and achieve system economy and reliability operation; more comprehensive goals and constraints are considered at multiple time and space scales, and more effective measures are taken to promote the balance of multi-dimensional comprehensive energy supply and demand. It also shows that as the time scale decreases, the deviation between the scheduling plan and the actual operation of the system will gradually decrease, verifying the feasibility and effectiveness of the strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 It is a structural diagram of a multi-micro energy network integrated energy system involved in the present invention.

[0097] Figure 2 to Figure 4 It is an initial load curve diagram of each micro energy network involved in the present invention.

[0098] Figure 5 This is a demand response structure diagram of a micro energy network involved in the present invention.

[0099] Figure 6 It is a multi-micro energy network integrated energy multi-temporal and spatial scale optimization framework diagram involved in the present invention.

[0100] Figure 7 It is a relevant energy price map involved in the present invention.

[0101] Figure 8 to Figure 11 This is a diagram of power optimization results of various systems in the residential micro-energy network involved in the present invention.

[0102] Fig.12 It is a diagram of the day-ahead energy interactive scheduling results of each micro-energy network involved in the present invention.

[0103] Figure 13 to Figure 16 This is a diagram of power optimization results of each system in a residential micro-energy network involved in the present invention.

[0104] Figures 17 to 20 The present invention relates to a real-time unbalanced power adjustment diagram of each system in a residential micro-energy network.

[0105] Figure 21 to Figure 24 The present invention relates to a load curve diagram for calling demand response at each stage of a residential micro-energy network. DETAILED DESCRIPTION

[0106] The present invention is further described below in conjunction with specific embodiments.

[0107] This implementation example is based on the actual project "Xiong'an New District Industrial Park" and is improved for case analysis. The industrial, commercial, and residential micro-energy grids all have a 120MW photovoltaic field and a 150MW wind farm; the maximum value of electricity and natural gas purchased by each micro-energy grid from the upper distribution network is 400kW. The example topology is as follows: Figure 1 The initial load curve is shown in Figure 2 to Figure 4 The load ratio is shown in Table 1, the compensation price of each dispatchable load participating in demand response is shown in Table 2, and the energy price is shown in Table 2. Figure 7 shown.

[0108] Table 1 User side load ratio

[0109] Load Type Normal load Replaceable load Transferable load Load reduction possible Electrical load 0.4 0.2 0.3 0.1 Heat load 0.6 0.1 0.2 0.1 Natural gas load 0.3 0.2 0.4 0.1 Hydrogen load 0.6 0.1 0.2 0.1

[0110] Table 2 Demand response compensation price

[0111]

[0112] A multi-time and space scale optimization scheduling method for micro-energy grid based on demand response, which models and analyzes each device in the micro-energy grid from the energy perspective, and constructs a multi-micro-energy grid integrated energy system model. Then, a multi-time and space scale operation model of a collaborative optimization framework consisting of three layers, upper, middle and lower, was established: the upper layer is the day-ahead scheduling stage, which schedules the entire integrated energy system; the middle layer is the intraday scheduling stage, which optimizes each micro-energy grid; the lower layer is the real-time scheduling stage, which adjusts the unbalanced power in each micro-energy grid; the three stages introduce replaceable, transferable and reducible loads respectively, and step-by-step participate in demand response optimization scheduling, which specifically includes the following steps:

[0113] 1) Model and analyze each device in the micro-energy grid from the energy perspective, and build a multi-micro-energy grid comprehensive energy system model:

[0114] ① CHP unit mathematical model:

[0115]

[0116]

[0117] In the formula, H chp Indicates the output thermal power of the CHP unit; Indicates the thermal-electric conversion efficiency of the CHP unit, which is 90%; P chp Indicates the power generation of the CHP unit; G chp Indicates the amount of natural gas consumed by the CHP unit; β chp Indicates the power generation efficiency of the CHP unit, which is 90%; H ng Indicates the lower calorific value of natural gas, taking 9.7kWh / m 3 ;H chp,i,max Indicates the maximum value of heat generated by the CHP unit;

[0118] ②Mathematical model of electric boiler:

[0119] H eb =η eb ·P eb

[0120]

[0121] In the formula, H eb Indicates the output thermal power of the electric boiler; β eb Indicates the heating efficiency of the electric boiler, which is 95%; P eb Indicates the electric power of the electric boiler; H eb,i,max Indicates the maximum value of the electric boiler power;

[0122] ③Mathematical model of water electrolysis device:

[0123]

[0124]

[0125]

[0126] In the formula, A ec Indicates the amount of hydrogen produced by the water electrolysis device; β ec Indicates the efficiency of the electrolysis device, the value is 90%; V ec Indicates the rated capacity of the electrolysis device; The fixed hydrogen production is represented by 350; P ecin Indicates the power consumption of the electrolysis device; P ec Indicates the rated power of the electrolysis device; a 0 、b 0 、c 0 is the efficiency function coefficient, with values ​​of 0.0109, 1.0676, and -0.0795 respectively; A ec,i,max Indicates the maximum power of hydrogen produced by electrolysis of water;

[0127] ④ Mathematical model of alkylation unit:

[0128]

[0129]

[0130] In the formula, G ch Indicates the amount of natural gas converted; β ch Indicates hydrogen conversion efficiency, the value is 73%; A ecch Indicates the amount of hydrogen required to produce natural gas; represents the molar mass conversion coefficient of hydrogen into natural gas; κ represents the gas mass per m3 of natural gas pipeline; G ch,i,max It indicates the maximum power of natural gas obtained after alkylation reaction;

[0131] ⑤Mathematical model of energy storage equipment:

[0132]

[0133] In the formula, x represents the type of energy storage, which are electric energy storage, thermal energy storage, natural gas energy storage and hydrogen energy storage; Respectively represent the energy state before and after energy charging and discharging; η x,c , η x,d and δ x They represent charging efficiency, discharging efficiency and energy loss coefficient respectively; Respectively represent the charging and discharging power at time t;

[0134] ⑥Alternative load mathematical model:

[0135] Taking the replaceable load of electricity / natural gas as an example, the micro-energy network can use gas boilers or electric boilers to supply heat load. In order to facilitate research, the marginal substitution rate of electricity / gas energy is proposed, which represents the ratio of the user's electricity consumption to the natural gas energy consumption in the same time period:

[0136]

[0137] P pl (t) = P buy0 (t)+β eg G rep (t)-P rep (t)

[0138] G pl (t) = G buy0 (t)+P rep (t) / β eg -G rep (t)

[0139] In the formula, ΔP rep (t), ΔG rep (t) respectively represent the changes in the demand for electricity and natural gas before and after the alternative load response; P buy0 (t), G buy0 (t) represent the initial electricity purchase and natural gas purchase of the system respectively; P rep (t), G rep (t) respectively represent the replaceable electricity load and natural gas load participating in the dispatch at time t;

[0140] ⑦Transferable load mathematical model:

[0141]

[0142]

[0143]

[0144]

[0145] Where P extr (t), H extr (t), G extr (t), A extr (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load before transfer; ΔP trans (t), ΔH trans (t), ΔG trans (t), ΔA trans (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load transferred by the user at time t; αt',t =1 means the load is transferred from time t' to time t, α t',t =-1 means the load changes from time t to time t';

[0146] ⑧Mathematical model of load reduction:

[0147] P xj (t) = P exxj (t)-α t ΔP xj (t)

[0148] H xj (t) = H exxj (t)-α t ΔH xj (t)

[0149] G xj (t) = G exxj (t)-α t ΔG xj (t)

[0150] A xj (t) = A exxj (t)-α t ΔA xj (t)

[0151] Where P exxj (t), H exxj (t), G exxj (t), A exxj (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load of the user before the response at time t; ΔP xj (t), ΔH xj (t), ΔG xj (t), ΔA xj (t) respectively represent the reduced electric load, thermal load, natural gas load and hydrogen load at time t; α t =1 means the load is reduced at time t, α t =0 means that the load is not reduced at time t;

[0152] 2) Establish a multi-temporal and spatial scale operation model of a collaborative optimization framework consisting of three layers: upper, middle and lower:

[0153] ①Upper layer:

[0154] a. Objective function:

[0155] The time scale of the day-ahead dispatch phase is 1 hour, and the operating cost C of the entire multi-microgrid integrated energy system is u Minimum is the goal, and the operating cost includes the energy purchase cost Environmental protection costs Equipment maintenance costs Alternative demand response costs Energy interaction cost between micro-energy grids

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162] In the formula, They represent electricity price and natural gas price respectively; T represents the dispatch time, which is 24h; They represent the amount of electricity and natural gas purchased by micro-energy grid i from the distribution network at time t, where i = 1 represents industrial micro-energy grid, i = 2 represents commercial micro-energy grid, and i = 3 represents residential micro-energy grid; α represents the unit CO 2 The processing cost is 0.031; β e , β g They represent the carbon emission coefficients of the power grid and natural gas grid in the micro-energy grid, and their values ​​are 0.972 and 0.23 respectively; R represents the equipment that needs to be maintained in the micro-energy grid i; P om,r , They represent the maintenance price and output power of unit r respectively; represent capacity compensation price and energy compensation price respectively; They represent the reserved response part and the actual response part of the load that can be reduced respectively; Respectively represent the sales prices of electricity, heat and natural gas at time t; They represent the interaction energy between micro-energy networks i and j respectively;

[0163] b. Constraints:

[0164] I. Energy purchase upper and lower limits:

[0165]

[0166] Where P b,i,max , G b,i,max They represent the upper limit of the purchase of electricity and gas by the micro-energy grid; P b,max , G b,maxThey represent the total upper limits of electricity and gas in the multi-micro-energy grid integrated energy system respectively;

[0167] II. Energy Interaction Constraints:

[0168]

[0169] Where P ij,max , H ij,max , G ij,max They represent the upper limits of the interaction of electricity, heat and natural gas energy between micro-energy grids i and j respectively; Respectively represent the direction of interaction of electricity, heat, and natural gas energy. A positive value indicates that energy flows from micro-energy grid i to j, and a negative value indicates that energy flows from micro-energy grid j to i.

[0170] III. Energy storage equipment operation constraints:

[0171]

[0172] In the formula, E x,i,min 、E x,i,max Respectively represent the upper and lower limits of the energy storage device; P x,c,i,max , P x,d,i,max Respectively represent the maximum value of charge and discharge energy; x,c,i , x,d,i Respectively represent the 0-1 state quantity of charging and discharging energy;

[0173] IV. Alternative load restraints:

[0174]

[0175] Where P rep,t,i,max , H rep,t,i,max , G rep,t,i,max , A rep,t,i,max They represent the maximum replacement amount of replaceable electricity, heat, natural gas and hydrogen load at time t respectively;

[0176] V. Energy balance constraints:

[0177]

[0178] In the formula, denote the predicted power of distributed generation and load respectively; The load change matrix representing the demand response; The variable matrices representing energy input, energy production, energy conversion, energy storage and energy interaction respectively; C in , C de , C tr , C s , C oc They represent the corresponding coupling matrices respectively;

[0179] ②Middle level:

[0180] a. Objective function:

[0181] The time scale of the intraday optimization scheduling stage is 15 minutes, which is consistent with the objective function of the day-ahead stage. Minimum is the objective function, and the cost includes the energy purchase cost Environmental protection costs Equipment maintenance costs Shiftable Demand Response Costs and energy interaction costs

[0182]

[0183]

[0184] b. Constraints:

[0185] In the intraday stage, the replaceable load adjusted in the day-ahead stage is taken as the known load, and on this basis, intraday optimization scheduling is performed, and the transferable load actively responds to demand, and the other constraints are the same;

[0186] Transferable loads can flexibly choose the load usage time according to the peak and valley periods of the micro-energy grid. Through time-of-use electricity prices and corresponding government incentives, the price changes of loads in different periods are guided, and users can adjust their energy consumption behavior accordingly.

[0187]

[0188] Where P trans,t,i,max , H trans,t,i,max , G trans,t,i,max , A trans,t,i,max They represent the maximum values ​​of electricity, heat, natural gas and hydrogen loads transferred at time t respectively;

[0189] ③Lower layer:

[0190] a. Objective function:

[0191] The time scale of the real-time optimization scheduling stage is 5 minutes, with real-time operation cost The goal is to minimize the imbalance between energy supply and demand; the cost includes the normal operating costs and adjustment costs Among them, this stage is to optimize the scheduling within the micro-energy grid. Energy interaction can be used as the power source of the micro-energy grid to provide energy for it, and it can also be used as a load to consume energy. Therefore, the loss cost of energy interaction will be considered;

[0192]

[0193]

[0194]

[0195]

[0196]

[0197] In the formula, represents the heat price; t s Indicates the start time of intraday optimization; N T represents the total number of optimization cycles within a day; γ e , γ h , γ g They represent the transmission loss coefficients of electricity, heat and natural gas, which are 6%, 8% and 3% respectively; μ p , Respectively represent the adjusted electricity price and the adjusted electricity purchase amount; μ g , Respectively represent the adjusted natural gas price and gas purchase volume; V represents the set in the real-time scheduling stage in micro-energy grid i; μ υ , They represent the adjusted price and adjusted power of the equipment υ respectively; Indicates that the demand response cost can be reduced; Δt is 5 min here;

[0198] b. Constraints:

[0199] In the real-time stage, the replaceable load after day-ahead optimization and the transferable load after intraday optimization are taken as known loads, and the load that can be reduced participates in demand response, and the other constraints are the same;

[0200] All types of loads that can be reduced should meet the following constraints:

[0201]

[0202] Where P xj,t,i,max , H xj,t,i,max , G xj,t,i,max , A xj,t,i,max They respectively represent the maximum values ​​of electricity, heat, natural gas and hydrogen loads that are reduced at time t.

[0203] Figure 5The response mechanism means that the energy aggregator exchanges information with the distribution network and the gas distribution network, and issues a demand scheduling task to the next-level micro-energy network in the day-ahead stage. The micro-energy network adjusts its reported energy consumption plan based on this scheduling, and the replaceable load is adjusted according to the scheduling requirements; in the intraday stage, the replaceable load adjusted in the day-ahead stage is regarded as a known load, and the transferable load is adjusted in active response; in the real-time stage, the demand load of the first two stages is regarded as a known load, and the reducible load is adjusted according to the scheduling needs.

[0204] Figure 8 to Figure 11 In the day-ahead phase, the multi-energy microgrid was optimally dispatched with a time scale of 1 hour. The energy dispatch effects of the four systems, namely, electricity, heat, natural gas, and hydrogen, reflected the supply and demand balance of multi-dimensional energy. Figure 8 23:00-9:00 is the low electricity consumption period with low electricity price. At this time, the micro-energy grid tends to purchase electricity from the distribution network to meet its own energy demand, and the shortfall can be met by WT and CHP. Fig. 9 10:00-21:00 is the low period of heat load for residents. At this time, electric boilers mainly meet the stable operation of the power system and produce less heat. CHP can meet the heat demand during this period. Fig.10 23:00-9:00 is the low electricity consumption period. The electrolysis device can operate at high power during this period to produce hydrogen to supply energy for the hydrogen load. Part of the hydrogen can enter the alkylation device to produce natural gas at this time. Fig.11 During the period from 23:00 to 9:00, the electrolysis device produces a large amount of hydrogen, which can be obtained through the alkylation device to meet its own natural gas needs; from 10:00 to 22:00, energy is purchased from the upper gas distribution network to make up for the natural gas shortage.

[0205] Fig.12 The energy interaction among the micro-energy grids is mainly concentrated between 21:00 and 7:00. 24:00-3:00 is the low energy consumption period of the residential micro-energy grid, and the commercial micro-energy grid is also closed at this time. Therefore, in addition to meeting its own electricity needs, it can sell electricity to the industrial micro-energy grid that is still producing at night to obtain profits; 21:00-7:00 is the closed period of the commercial micro-energy grid, and the demand for thermal energy is not high at this time. Therefore, during this period, the commercial micro-energy grid sells thermal energy to the residential micro-energy grid that needs heating and the industrial micro-energy grid that produces at night; the commercial micro-energy grid and the residential micro-energy grid have less demand for natural gas energy from 23:00 to 5:00, which can be sold to the industrial micro-energy grid with a large demand for gas for production, and they can also make profits.

[0206] Figure 13 to Figure 16 In the intra-day phase, the multi-energy microgrid is optimized for 15-minute time scale, and the energy dispatch details of the four systems, namely, electricity, heat, natural gas, and hydrogen, are more specific. However, there are still some differences compared with the day-ahead dispatch phase, such as Fig.13 In the time periods of 12:00-14:00 and 18:00-20:00, the power consumption of electric boilers is reduced compared with the previous stage, and the purchase of electricity from the distribution network is reduced or zero, which reduces the cost of purchasing energy during peak hours; Fig.14 In the process, energy interaction arrived earlier than the previous stage at 19:00. This is because the heat output of the electric boiler decreased, and the heat output of the CHP unit could not meet the demand, and the surplus heat came from the other two micro-energy grids.

[0207] Figures 17 to 20 By introducing the reducible load to coordinate the dispatch and real-time adjustment of each specific part of the residential micro-energy network, the unbalanced power introduced by the electricity, heat, hydrogen and natural gas systems in the micro-energy network can be effectively covered. If the actual value of PV and WT increases / decreases at a certain moment, the actual value of the load increases / decreases, the remaining energy equipment will adjust the output value, and the demand response load will increase / decrease power according to demand to make up the difference.

[0208] Figure 21 to Figure 24 It shows that by introducing replaceable loads in the day-ahead stage, users can randomly choose according to energy prices or energy demand to achieve their own goals, which can improve the system's renewable energy consumption capacity. For example, from 12:00 to 13:00, the natural gas load is reduced by about 15% compared with the original curve, and the electric load does not change at this time. From 13:00 to 14:00, the electric load is reduced by about 18.2% compared with the original curve, and the natural gas load curve rises by about 15% to the original curve, which fully reflects the substitutability of energy. In the intraday stage, such as from 13:00 to 14:00, the electric load Compared with the day-ahead stage, it is reduced by about 7.68% to 10.63%, and the load curve in this section is lower than the day-ahead curve. During 22:00-24:00, the electric load increases by about 4.76% compared with the day-ahead stage, and is higher than the day-ahead curve during this period. The real-time stage introduces reducible loads to further correct the load curve and reduce load usage during peak energy consumption periods. For example, from 11:00 to 13:00, the electric load is reduced by about 2.59% to 10.7% compared with the intraday stage, and the load curve during this period is lower than the intraday load curve, reducing the pressure during peak energy consumption periods.

[0209] The scope of protection claimed by the present invention is not limited to the above specific embodiments. For those skilled in the art, the present invention may have various deformations and changes. Any modifications, improvements and equivalent substitutions made within the concept and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-temporal and spatial scale optimization scheduling method for micro-energy grid based on demand response, characterized by: From the energy perspective, a multi-dimensional energy supply and demand balance model is constructed; a multi-temporal and spatial scale operation model with coordinated optimization of the upper, middle and lower layers is established. The upper layer adopts day-ahead scheduling, the middle layer adopts intraday scheduling, and the lower layer adopts real-time scheduling. The three scheduling stages introduce replaceable loads, transferable loads and reducible loads for demand response optimization, which specifically includes the following steps: 1) Model and analyze each device in the micro-energy grid from the energy perspective, and build a multi-micro-energy grid comprehensive energy system model: ① CHP unit mathematical model: In the formula, H chp Indicates the output thermal power of the CHP unit; Indicates the thermal-electric conversion efficiency of the CHP unit, which is 90%; P chp Indicates the power generation of the CHP unit; G chp Indicates the amount of natural gas consumed by the CHP unit; β chp Indicates the power generation efficiency of the CHP unit, which is 90%; H ng Indicates the lower calorific value of natural gas, taking 9.7kWh / m 3 ;H chp,i,max Indicates the maximum value of heat generated by the CHP unit; ②Mathematical model of electric boiler: H eb =the eb ·P eb In the formula, H eb Indicates the output thermal power of the electric boiler; η eb Indicates the heating efficiency of the electric boiler, which is 95%; P eb Indicates the electric power of the electric boiler; H eb,i,max Indicates the maximum value of the electric boiler power; ③Mathematical model of water electrolysis device: In the formula, A ec Indicates the amount of hydrogen produced by the water electrolysis device; β ec Indicates the efficiency of the electrolysis device, the value is 90%; V ec Indicates the rated capacity of the electrolysis device; The fixed hydrogen production is represented by 350; P ecin Indicates the power consumption of the electrolysis device; P ec represents the rated power of the electrolysis device; a0, b0, c0 are efficiency function coefficients, with values ​​of 0.0109, 1.0676, -0.0795 respectively; A ec,i,max Indicates the maximum power of hydrogen produced by electrolysis of water; ④ Mathematical model of alkylation unit: In the formula, G ch Indicates the amount of natural gas converted; β ch Indicates hydrogen conversion efficiency, the value is 73%; A ecch Indicates the amount of hydrogen required to produce natural gas; M H2 It represents the molar mass conversion coefficient of hydrogen into natural gas; κ represents the mass per m of natural gas pipeline. 3 Corresponding gas mass; G ch,i,max Indicates the maximum power of natural gas obtained after alkylation reaction; H ng Indicates the lower calorific value of natural gas, taking 9.7kWh / m 3 ; ⑤Mathematical model of energy storage equipment: In the formula, x represents the type of energy storage, which are electric energy storage, thermal energy storage, natural gas energy storage and hydrogen energy storage; Respectively represent the energy state before and after energy charging and discharging; η x,c , η x,d and δ x They represent charging efficiency, discharging efficiency and energy loss coefficient respectively; Respectively represent the charging and discharging power at time t; ⑥Alternative load mathematical model: Taking the replaceable load of electricity / natural gas as an example, the micro-energy network can use gas boilers or electric boilers to supply heat load. In order to facilitate research, the marginal substitution rate of electricity / gas energy is proposed, which represents the ratio of the user's electricity consumption to the natural gas energy consumption in the same time period: P pl (t)=P buy0 (t)+β eg G rep (t)-P rep (t) G pl (t)=G buy0 (t)+P rep (t) / β eg -G rep (t) Where ΔP rep , ΔG rep Respectively represent the changes in electricity and natural gas demand before and after the alternative load response; P buy0 (t), G buy0 (t) represent the initial electricity purchase and natural gas purchase of the system respectively; P rep (t), G rep (t) respectively represent the replaceable electricity load and natural gas load participating in the dispatch at time t; ⑦Transferable load mathematical model: Where P extr (t), H extr (t), G extr (t), A extr (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load before transfer; ΔP trans (t), ΔH trans (t), ΔG trans (t), ΔA trans (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load transferred by the user at time t; α t',t =1 means the load is transferred from time t' to time t, α t',t =-1 means the load changes from time t to time t'; ⑧Mathematical model of load reduction: P xj (t)=P exxj (t)-α t ΔP xj (t) H xj (t)=H exxj (t)-α t ΔH xj (t) G xj (t)=G exxj (t)-α t ΔG xj (t) A xj (t)=A exxj (t)-α t ΔA xj (t) Where P exxj (t), H exxj (t), G exxj (t), A exxj (t) respectively represent the electric load, thermal load, natural gas load and hydrogen load of the user before the response at time t; ΔP xj (t), ΔH xj (t), ΔG xj (t), ΔA xj (t) respectively represent the reduced electric load, thermal load, natural gas load and hydrogen load at time t; α t =1 means the load is reduced at time t, α t =0 means that the load is not reduced at time t; 2) Establish a multi-temporal and spatial scale operation model of a collaborative optimization framework consisting of three layers: upper, middle and lower: ①Upper layer: a. Objective function: The time scale of the day-ahead dispatch phase is 1 hour, and the operating cost C of the entire multi-microgrid integrated energy system is u Minimum is the goal, and the operating cost includes the energy purchase cost Environmental protection costs Equipment maintenance costs Alternative demand response costs Energy interaction cost between micro-energy grids In the formula, They represent electricity price and natural gas price respectively; T represents the dispatch time, which is 24h; They represent the amount of electricity and natural gas purchased by micro-energy grid i from the distribution network at time t, where i = 1 represents industrial micro-energy grid, i = 2 represents commercial micro-energy grid, and i = 3 represents residential micro-energy grid; α represents the unit CO2 processing cost, which is 0.031; β e , β g They represent the carbon emission coefficients of the power grid and natural gas grid in the micro-energy grid, and their values ​​are 0.972 and 0.23 respectively; R represents the equipment that needs to be maintained in the micro-energy grid i; P om,r , They represent the maintenance price and output power of unit r respectively; represent capacity compensation price and energy compensation price respectively; They represent the reserved response part and the actual response part of the load that can be reduced respectively; Respectively represent the sales prices of electricity, heat and natural gas at time t; They represent the interaction energy between micro-energy networks i and j respectively; b. Constraints: Ⅰ. Energy purchase upper and lower limits: Where P b,i,max , G b,i,max They represent the upper limits of electricity and gas purchases of Micro Energy Grid i, respectively; P b,max , G b,max They represent the total upper limits of electricity and gas in the multi-micro-energy grid integrated energy system respectively; II. Energy interaction constraints: Where P ij,max , H ij,max , G ij,max They represent the upper limits of the interaction of electricity, heat and natural gas energy between micro-energy grids i and j respectively; Respectively represent the direction of interaction of electricity, heat, and natural gas energy. A positive value indicates that energy flows from micro-energy grid i to j, and a negative value indicates that energy flows from micro-energy grid j to i. III. Energy storage equipment operation constraints: In the formula, E x,i,min 、E x,i,max Respectively represent the upper and lower limits of the energy storage device; P x,c,i,max , P x,d,i,max Respectively represent the maximum value of charge and discharge energy; x,c,i , x,d,i Respectively represent the 0-1 state quantity of charging and discharging energy; IV. Alternative load constraints: Where P rep,t,i,max , H rep,t,i,max , G rep,t,i,max , A rep,t,i,max They represent the maximum replacement amount of replaceable electricity, heat, natural gas and hydrogen load at time t respectively; Ⅴ. Energy balance constraints: In the formula, denote the predicted power of distributed generation and load respectively; The load change matrix representing the demand response; The variable matrices representing energy input, energy production, energy conversion, energy storage and energy interaction respectively; C in,i , C de,i , C tr,i , C s,i , C oc,i They represent the corresponding coupling matrices respectively; ②Middle level: a. Objective function: The time scale of the intraday optimization scheduling stage is 15 minutes, which is consistent with the objective function of the day-ahead stage. Minimum is the objective function, and the cost includes the energy purchase cost Environmental protection costs Equipment maintenance costs Shiftable Demand Response Costs and energy interaction costs b. Constraints: In the intraday stage, the replaceable load adjusted in the day-ahead stage is taken as the known load, and on this basis, intraday optimal scheduling is performed, and the transferable load actively responds to demand, and the other constraints are the same; Transferable loads can flexibly choose the load usage time according to the peak and valley periods of the micro-energy grid. Through time-of-use electricity prices and corresponding government incentives, the price changes of loads in different periods are guided, and users can adjust their energy consumption behavior accordingly. Where P trans,t,i,max , H trans,t,i,max , G trans,t,i,max , A trans,t,i,max They represent the maximum values ​​of electricity, heat, natural gas and hydrogen loads transferred at time t respectively; ③Lower layer: a. Objective function: The time scale of the real-time optimization scheduling stage is 5 minutes, with real-time operation cost The goal is to minimize the imbalance between energy supply and demand; the cost includes the normal operating costs and adjustment costs Among them, this stage is to optimize the scheduling within the micro-energy grid. Energy interaction can be used as the power source of the micro-energy grid to provide energy for it, and it can also be used as a load to consume energy. Therefore, the loss cost of energy interaction will be considered; In the formula, represents the heat price; t s Indicates the start time of intraday optimization; N T represents the total number of optimization cycles within a day; γ e , γ h , γ g They represent the transmission loss coefficients of electricity, heat and natural gas, which are 6%, 8% and 3% respectively; μ p , Respectively represent the adjusted electricity price and the adjusted electricity purchase amount; μ g , Respectively represent the adjusted natural gas price and gas purchase volume; V represents the set in the real-time scheduling stage in micro-energy grid i; μ υ , They represent the adjusted price and adjusted power of the equipment υ respectively; Indicates that the demand response cost can be reduced; Δt is 5 min here; b. Constraints: In the real-time stage, the replaceable load after day-ahead optimization and the transferable load after intraday optimization are taken as known loads, and the load that can be reduced participates in demand response, and the other constraints are the same; All types of loads that can be reduced should meet the following constraints: Where P xj,t,i,max , H xj,t,i,max , G xj,t,i,max , A xj,t,i,max They respectively represent the maximum values ​​of electricity, heat, natural gas and hydrogen loads that are reduced at time t.

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