Multi-energy system optimization scheduling method and device considering supply and demand bidirectional demand response

Abstract

The present invention discloses a multi-energy system optimization scheduling method that takes into account bidirectional supply and demand response, including the following steps: determining and obtaining basic operating parameters of the integrated energy system; modeling the integrated energy system considering multiple energy flows, including electricity and heat; constructing a comprehensive demand response model on the energy consumption side that considers both electrical and thermal loads; constructing a demand response model on the energy supply side that considers wind power, hydrogen production, and hydrogen storage tanks; constructing an objective function that minimizes energy satisfaction and operating costs for the integrated energy system; establishing constraints such as energy balance constraints, system network constraints, and equipment model constraints; and solving for a day-ahead optimized scheduling plan for the integrated energy system. This method can achieve supply and demand balance and economic operation of the integrated energy system through the complementary cooperation of multiple energy flows, while satisfying both electrical and thermal load requirements.

Description

Technical Field

[0001] The present invention relates to an optimized scheduling scheme for an integrated energy system, and in particular to an optimized scheduling method and device for a multi-energy system taking into account a supply-demand bidirectional demand response. Background Art

[0002] The Energy Internet is an interconnected network of various energy sources, built around the power system. It is a new generation of energy system that achieves multi-energy complementarity and coordination, clean, low-carbon, safe and efficient development. The deep integration of energy systems and the Internet has become an important energy strategic requirement and a development trend for related industries in my country. Understanding the dynamic response characteristics of resources plays an important role in flexibly regulating resources and promoting the safe and stable operation of the Energy Internet. Existing technologies primarily consider demand response on the energy consumption side, with less consideration given to the response characteristics on the energy supply side. This neglects the role of comprehensive consideration of the two-way demand response characteristics of supply and demand in improving the flexibility and economy of optimized scheduling of multi-energy systems. Summary of the Invention

[0003] The present invention aims to overcome the shortcomings of the aforementioned background technology by proposing a multi-energy system optimization scheduling method that takes into account bidirectional supply and demand response. This method utilizes energy equipment such as cogeneration units, heat pumps, gas turbines, hydrogen production, and hydrogen storage tanks to construct a comprehensive energy system optimization scheduling model that takes into account bidirectional supply and demand response. This model can achieve the complementary and synergistic effects of multiple energy flows, increasing the system's energy supply diversity and overall economic efficiency.

[0004] In order to solve the technical problem, the present invention adopts the following technical solution:

[0005] The multi-energy system optimization scheduling method considering the supply and demand bidirectional demand response includes the following steps:

[0006] Determine and obtain basic operating parameters of the integrated energy system;

[0007] Construct a comprehensive energy system model that considers electricity and heat multi-energy flows;

[0008] Construct a comprehensive demand response model for energy consumption that takes into account both electrical and thermal loads;

[0009] Construct a supply-side demand response model that considers wind power, hydrogen production, and hydrogen storage tanks;

[0010] Construct an objective function to minimize energy satisfaction and operating costs of the integrated energy system;

[0011] Obtain constraints for energy balance constraints, system network constraints, and device model constraints;

[0012] The objective function is solved to obtain the day-ahead optimal scheduling plan for the integrated energy system.

[0013] Furthermore, a comprehensive energy system model considering electricity and heat multi-energy flows is constructed, which includes the following steps:

[0014] Grid modeling for radial distribution network: Dist-Flow power flow equation is used to establish the network model of distribution network, including active power flow equation, reactive power flow equation and voltage equation, namely:

[0015]

[0016]

[0017]

[0018] Among them, P i,t and Q i,t are the active power and reactive power on the line from grid node i to node i+1 at time t; r i is the resistance of the line from node i to node i+1 at time t; and are the active load and power source active power at grid node i at time t; x i is the reactance of the line from node i to node i+1 at time t; and are the reactive load and power supply reactive power at grid node i at time t respectively; V i,t is the voltage of node i at time t;

[0019] Introducing the variables shown in formula (4), performing second-order cone relaxation on the distribution network model, we get formula (5):

[0020]

[0021]

[0022] Heat network modeling: The heat change relationship of pipeline ij can be expressed as:

[0023]

[0024] Where H i,t and H j,t are the heat energy flows at node i and node j at time t in the heat network system; L ij is the length of the pipe ij; ∑R unit is the thermal resistance per unit length from the heat medium to the ambient medium; T ij，t is the heat medium temperature of pipe ij at time t in the heat network system; T a,t is the ambient temperature of the pipe; c is the specific heat capacity of water; m i,t and m j,tare the heat medium mass flows at nodes i and j respectively; T i,t and T j,t are the heat medium temperatures at node i and node j respectively;

[0025] The heat transfer loss in the pipeline due to the temperature difference between the pipeline and the surrounding environment, the temperature at the end of the pipeline can be expressed as:

[0026]

[0027]

[0028] Among them, α ij is the loss constant of pipeline ij; k ij is the heat loss coefficient of pipeline ij; m ij，t is the heat medium mass flow in pipe ij at time t in the heat network system.

[0029] Furthermore, a comprehensive demand response model for energy consumption considering both electrical and thermal loads is constructed, which includes the following steps:

[0030] Electric load demand response modeling:

[0031]

[0032]

[0033]

[0034] in, and They represent the fixed load, transferable load and total load at grid node i at time t respectively; represents the upper limit of the transferable electric load at grid node i; represents the total amount of transferable electric load at grid node i during the dispatch period;

[0035] Heat Load Demand Response Modeling:

[0036]

[0037]

[0038]

[0039] in, and They represent the fixed heat load, transferable heat load and total heat load at the node k of the heating network at time t respectively; represents the upper limit of the transferable heat load at the heating network node k; It represents the total amount of heat load that can be transferred at node k in the heating network during the scheduling period.

[0040] Furthermore, a supply-side demand response model considering wind power, hydrogen production, and hydrogen storage tanks is constructed, including the following steps:

[0041] Constructing electric hydrogen production and hydrogen storage tank models:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] I ch,t,i +I dis,t,i ≤1 (21)

[0049] in, and H 2t,i are the power consumption and hydrogen production power of the electrolyzer at grid node i at time t; η i represents the hydrogen production efficiency of the electrolyzer at grid node i; represents the maximum power consumption of the electrolyzer at grid node i; and are the maximum power of hydrogen filling and hydrogen discharge of the hydrogen storage tank at grid node i, respectively; and are the power of hydrogen filling and hydrogen discharging at the hydrogen storage tank at grid node i at time t, respectively; and are the hydrogen storage capacity of the hydrogen storage tank at grid node i at time t-1 and t respectively; are the minimum and maximum hydrogen storage capacities of the hydrogen storage tank at grid node i; α p2h,i is the loss rate of the hydrogen storage tank at grid node i; η ch,i and η dis,i are the hydrogen energy storage efficiency and hydrogen energy release efficiency of the hydrogen storage tank at grid node i, respectively; I ch,t,i and I dis,t,i is a 0-1 variable indicating whether the hydrogen storage tank at grid node i is in energy storage or release state at time t;

[0050] Wind power dispatch constraint formulation:

[0051]

[0052]

[0053]

[0054] in, represents the predicted wind power at grid node i at time t; represents the actual wind power at grid node i at time t, and They represent the wind power parts participating in hydrogen production and system dispatch at grid node i at time t respectively.

[0055] Furthermore, the objective function of energy satisfaction and minimum operating cost of the integrated energy system is constructed, which includes the following steps:

[0056] Operation cost objective function formulation:

[0057] For the types of units involved, operating costs include equipment energy supply costs and electricity purchase and sales costs:

[0058]

[0059] Among them, C energy The cost of supplying energy to the system, is the equipment operating cost of unit n during period t, where m3 = {GT, GB, CHP, WT, ESS, TES, HES}, where GT, GB, WT, CHP, ESS, TES, and HES are gas turbines, gas boilers, wind power, combined heat and power units, electric energy storage, heat storage tanks, and hydrogen storage tanks, respectively; N m3 is the total number of energy supply units; is the cost of electricity purchase and sale in period t; is the cost of purchasing hydrogen during period t;

[0060]

[0061] in, are the purchased power and sold power from the upper power grid during period t, are the electricity purchase price and electricity sales price at time t respectively;

[0062]

[0063] Among them, p t ,λ t are the hydrogen purchasing power and hydrogen purchasing price in period t respectively;

[0064] Setting of penalty costs for wind curtailment:

[0065]

[0066] Among them, C punis represents the penalty cost for wind curtailment; cwt represents the wind curtailment penalty coefficient;

[0067] User energy satisfaction objective function formulation:

[0068]

[0069] Among them, C dr represents the penalty cost for wind curtailment; v e , α e is the electricity preference coefficient, v h , α h is the heat preference coefficient;

[0070] Comprehensive optimization objective function formulation:

[0071] minF=min(C energy +C punis +C dr ) (30)

[0072] Among them, F is the comprehensive cost of the system.

[0073] Furthermore, the equipment model constraints include upper and lower limit constraints on unit output, unit ramp constraints, energy storage equipment constraints, and energy conversion constraints on extraction-condensing thermal power units.

[0074] Furthermore, the commercial solver CPLEX is used to solve the integrated energy system model.

[0075] A multi-energy system optimization dispatching device that takes into account supply and demand bidirectional demand response, including:

[0076] Parameter acquisition module, used to determine and obtain basic operating parameters of the integrated energy system;

[0077] Integrated energy system model building module, used to build an integrated energy system model considering electricity, heat and other multi-energy flows;

[0078] The energy consumption side comprehensive demand response model construction module is used to construct the energy consumption side comprehensive demand response model considering the electrical load and thermal load;

[0079] Energy supply side demand response model construction module, used to build an energy supply side demand response model considering wind power, electric hydrogen production and hydrogen storage tanks;

[0080] Objective function construction module, used to construct the objective function of minimizing energy satisfaction and operating cost of the integrated energy system;

[0081] Constraint condition acquisition module, used to obtain the constraints of energy balance constraints, system network constraints, and device model constraints;

[0082] The objective function solving module is used to solve the objective function and obtain the day-ahead optimal scheduling plan for the integrated energy system.

[0083] A computing device comprises: one or more processing units; a storage unit for storing one or more programs, wherein when the one or more programs are executed by the one or more processing units, the one or more processing units execute the multi-energy system optimization scheduling method taking into account the supply and demand bidirectional demand response as described above.

[0084] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of the multi-energy system optimization scheduling method taking into account the supply and demand bidirectional demand response are implemented as described above.

[0085] Compared with existing technologies, this solution adopts a multi-energy system optimization scheduling method that takes into account the two-way demand response of supply and demand, achieving the following beneficial effects:

[0086] (1) The proposed optimization scheduling method can promote the complementary cooperation of multiple energy flows and achieve the supply and demand balance and economic operation of the integrated energy system under the premise of meeting the demands of electricity load, heat load and hydrogen load;

[0087] (2) The proposed optimization scheduling method realizes peak shaving and valley filling of electric and thermal loads through comprehensive demand response on the energy consumption side. At the same time, electrolyzers and hydrogen storage tanks are introduced to consider demand response on the energy supply side, increase energy supply diversity for the integrated energy system, optimize wind power scheduling strategies, and improve wind power consumption levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 Develop a flow chart for the optimal dispatching method of a multi-energy system taking into account supply and demand bidirectional demand response;

[0089] Figure 2 A network diagram of an integrated energy system according to an embodiment of the present invention;

[0090] Figure 3 This is a load and wind power output curve diagram of an embodiment of the present invention;

[0091] Figure 4 This is a comparison before and after of the electric load demand response of an embodiment of the present invention;

[0092] Figure 5 This is a comparison before and after of the heat load demand response of an embodiment of the present invention;

[0093] Figure 6 A comparison of wind power consumption in different scenarios according to an embodiment of the present invention;

[0094] Figure 7 A wind power dispatching diagram according to an embodiment of the present invention;

[0095] Figure 8 The dispatching power balance optimization result of the embodiment of the present invention;

[0096] Figure 9 The scheduling thermal balance optimization result of the embodiment of the present invention;

[0097] Figure 10 This is the scheduling hydrogen energy balance optimization result of an embodiment of the present invention. DETAILED DESCRIPTION

[0098] The present invention provides a multi-energy system optimization scheduling method that takes into account the two-way demand response of supply and demand. The method constructs an integrated energy system optimization scheduling model through energy equipment such as cogeneration units, heat pumps, gas turbines, electric hydrogen production and hydrogen storage tanks. First, a comprehensive energy system model of electric-thermal multi-energy flows is constructed; then, a comprehensive demand response model of the energy consumption side considering electric load and thermal load and a demand response model of the energy supply side considering wind power, electric hydrogen production and hydrogen storage tanks are constructed respectively; finally, with the lowest operating cost of the integrated energy system and energy satisfaction as the objective function, and with energy balance constraints and network constraints as constraints, a day-ahead optimization scheduling scheme for the integrated energy system is proposed. The simulation results of the example show that the proposed optimization scheduling method can achieve the supply and demand balance and economic operation of the integrated energy system through the complementary cooperation of multiple energy flows under the premise of meeting the demand of electric load and thermal load.

[0099] The multi-energy system optimization scheduling method considering the supply and demand bidirectional demand response includes the following steps:

[0100] Determine and obtain basic operating parameters of the integrated energy system;

[0101] Construct a comprehensive energy system model that considers electricity and heat multi-energy flows;

[0102] Construct a comprehensive demand response model for energy consumption that takes into account both electrical and thermal loads;

[0103] Construct a supply-side demand response model that considers wind power, hydrogen production, and hydrogen storage tanks;

[0104] Construct an objective function to minimize energy satisfaction and operating costs of the integrated energy system;

[0105] Obtain constraints for energy balance constraints, system network constraints, and device model constraints;

[0106] The objective function is solved to obtain the day-ahead optimal scheduling plan for the integrated energy system.

[0107] Furthermore, a comprehensive energy system model considering electricity and heat multi-energy flows is constructed, which includes the following steps:

[0108] Grid modeling for radial distribution network: Dist-Flow power flow equation is used to establish the network model of distribution network, including active power flow equation, reactive power flow equation and voltage equation, namely:

[0109]

[0110]

[0111]

[0112] Among them, P i,t and Q i,t are the active power and reactive power on the line from grid node i to node i+1 at time t; r i is the resistance of the line from node i to node i+1 at time t; and are the active load and power source active power at grid node i at time t; x i is the reactance of the line from node i to node i+1 at time t; and are the reactive load and power supply reactive power at grid node i at time t respectively; V i,t is the voltage of node i at time t;

[0113] Introducing the variables shown in formula (4), performing second-order cone relaxation on the distribution network model, we get formula (5):

[0114]

[0115]

[0116] Heat network modeling: The heat change relationship of pipeline ij can be expressed as:

[0117]

[0118] Where H i,t and H j,t are the heat energy flows at node i and node j at time t in the heat network system; L ij is the length of the pipe ij; ∑R unit is the thermal resistance per unit length from the heat medium to the ambient medium; T ij，t is the heat medium temperature of pipe ij at time t in the heat network system; T a,t is the ambient temperature of the pipe; c is the specific heat capacity of water; m i,t and m j,t are the heat medium mass flows at nodes i and j respectively; T i,t and T j,t are the heat medium temperatures at node i and node j respectively;

[0119] The heat transfer loss in the pipeline due to the temperature difference between the pipeline and the surrounding environment, the temperature at the end of the pipeline can be expressed as:

[0120]

[0121]

[0122] Among them, α ij is the loss constant of pipeline ij; k ij is the heat loss coefficient of pipeline ij; m ij，t is the heat medium mass flow in pipe ij at time t in the heat network system.

[0123] Furthermore, a comprehensive demand response model for energy consumption considering both electrical and thermal loads is constructed, which includes the following steps:

[0124] Electric load demand response modeling:

[0125]

[0126]

[0127]

[0128] in, and They represent the fixed load, transferable load and total load at grid node i at time t respectively; represents the upper limit of the transferable electric load at grid node i; represents the total amount of transferable electric load at grid node i during the dispatch period;

[0129] Heat Load Demand Response Modeling:

[0130]

[0131]

[0132]

[0133] in, and They represent the fixed heat load, transferable heat load and total heat load at the node k of the heating network at time t respectively; represents the upper limit of the transferable heat load at the heating network node k; It represents the total amount of heat load that can be transferred at node k in the heating network during the scheduling period.

[0134] Furthermore, a supply-side demand response model considering wind power, hydrogen production, and hydrogen storage tanks is constructed, including the following steps:

[0135] Constructing electric hydrogen production and hydrogen storage tank models:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] I ch,t,i +I dis,t,i ≤1 (21)

[0143] in, and H 2t,i are the power consumption and hydrogen production power of the electrolyzer at grid node i at time t; η i represents the hydrogen production efficiency of the electrolyzer at grid node i; represents the maximum power consumption of the electrolyzer at grid node i; and are the maximum power of hydrogen filling and hydrogen discharge of the hydrogen storage tank at grid node i, respectively; and are the power of hydrogen filling and hydrogen discharging at the hydrogen storage tank at grid node i at time t, respectively; and are the hydrogen storage capacity of the hydrogen storage tank at grid node i at time t-1 and t respectively; are the minimum and maximum hydrogen storage capacities of the hydrogen storage tank at grid node i; α p2h,i is the loss rate of the hydrogen storage tank at grid node i; η ch,i and η dis,i are the hydrogen energy storage efficiency and hydrogen energy release efficiency of the hydrogen storage tank at grid node i, respectively; I ch,t,i and I dis,t,i is a 0-1 variable indicating whether the hydrogen storage tank at grid node i is in energy storage or release state at time t;

[0144] Wind power dispatch constraint formulation:

[0145]

[0146]

[0147]

[0148] in, represents the predicted wind power at grid node i at time t; represents the actual wind power at grid node i at time t, and They represent the wind power parts participating in hydrogen production and system dispatch at grid node i at time t respectively.

[0149] Furthermore, the objective function of energy satisfaction and minimum operating cost of the integrated energy system is constructed, which includes the following steps:

[0150] Operation cost objective function formulation:

[0151] For the types of units involved, GT, GB, WT, CHP, ESS, TES, and HES are gas turbines, gas boilers, wind power, combined heat and power units, electric energy storage, heat storage tanks, and hydrogen storage tanks, respectively; operating costs include equipment energy supply costs and electricity purchase and sales costs:

[0152]

[0153] Among them, C energy The cost of supplying energy to the system, is the equipment operating cost of unit n in period t, where m3 = {GT, GB, CHP, WT, ESS, TES, HES}, which means that the operating costs of the gas turbine, gas boiler, wind power, cogeneration unit, electric energy storage, heat storage tank, and hydrogen storage tank need to be calculated; is the total number of energy supply units; is the cost of electricity purchase and sale in period t; is the cost of purchasing hydrogen during period t;

[0154]

[0155] in, are the purchased power and sold power from the upper power grid during period t, are the electricity purchase price and electricity sales price at time t respectively;

[0156]

[0157] Among them, p t ,λ t are the hydrogen purchasing power and hydrogen purchasing price in period t respectively;

[0158] Setting of penalty costs for wind curtailment:

[0159]

[0160] Among them, C punis represents the penalty cost for wind curtailment; c wt represents the wind curtailment penalty coefficient;

[0161] User energy satisfaction objective function formulation:

[0162]

[0163] Among them, C dr represents the penalty cost for wind curtailment; v e , α e is the electricity preference coefficient, v h , α h is the heat preference coefficient;

[0164] Comprehensive optimization objective function formulation:

[0165] minF=min(C energy +C punis +C dr ) (30)

[0166] Among them, F is the comprehensive cost of the system.

[0167] Furthermore, the energy balance constraint, system network constraint, and device model constraint are constructed, and the formulation is carried out in the following steps:

[0168] The energy balance constraint of the constructed system is:

[0169] The energy balance constraints for electricity, heat and hydrogen need to be met separately during system operation:

[0170]

[0171]

[0172]

[0173] in, are the electric power generated by the gas turbine and wind power at the grid node i during period t respectively; is the thermal power of the gas turbine at the node k in the heating network during period t; is the electric power exchanged between the grid node i and the external grid during period t; is the electrical load at the grid node i during period t; are the electrical output and thermal output of the CHP unit connected to the grid node i and the heat network node k during period t, respectively; is the hydrogen purchase power at grid node i during period t; is the hydrogen load at grid node i during period t; are the power of the electric storage device and the heat storage device connected to the grid node i and the heat network node k in period t respectively;

[0174] The constructed system network constraints are:

[0175] Grid constraints:

[0176] V i min ≤V i,t ≤V i max (34)

[0177]

[0178] Among them, V i min and V i max is the minimum and maximum allowable voltage of node i; V i,t is the voltage of node i at time t; I ij,t is the current flowing through line ij at time t; is the maximum current allowed to flow through line ij.

[0179] Thermal network constraints:

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187] in, The upper and lower limits of supply and return water temperature; is the supply and return water temperature of node k during period t; is the heat output of the heat source node and the heat exchange amount of the heat exchange station node; is the mass flow of heat medium flowing into node k during period t; q t,k is the mass flow rate of heat medium flowing into node k during period t; is the set of pipelines with node k as the head and end points; T t,k is the mixed temperature at node k and the inlet temperature of the lower-level pipe at period t.

[0188] The constraints of the constructed device model are:

[0189] Upper and lower limit constraints of unit output:

[0190]

[0191] in, are the upper and lower limits of the unit n electrical output, where m4 = {GT, GB, WT, CHP}, meaning that the gas turbine, gas boiler, wind power, and cogeneration units must all meet the upper and lower limits of the unit output;

[0192] Unit climbing constraints, namely:

[0193]

[0194] in, and is the ramp rate and ramp rate of unit n, where m6 = {GT, GB, CHP}, that is, the gas turbine, gas boiler, and cogeneration unit must all meet the unit ramp constraints;

[0195] Energy storage device constraints, namely:

[0196] To ensure scheduling consistency, the energy storage device must maintain the same storage capacity at the start and end of scheduling, and the energy storage and release processes cannot occur simultaneously. The constraints of the electrical energy storage device are shown in Equation (45). The principle of thermal energy storage is similar and will not be repeated here.

[0197]

[0198] in, P is the charging and discharging power of the energy storage device during period t; t ESS P is the storage capacity of the energy storage device during period t; ESS,max 、P ESS,min The upper and lower limits of the storage power of the energy storage equipment; and They are and The power limit of and are the discharge and charge state variables respectively.

[0199] The energy conversion constraints of the extraction-condensing thermal power unit are:

[0200]

[0201] in, is the minimum and maximum electrical output of unit n under condensing conditions; is the upper limit of heat output of unit n; c m,n , K m,n 、c v,n is the unit constant.

[0202] Furthermore, combining the objective function and constraints, it can be seen that the integrated energy system optimization scheduling model is a mixed integer nonlinear programming problem, and the commercial solver CPLEX is called to solve the model.

[0203] This plan includes the following:

[0204] (1) Composition of the integrated energy system

[0205] The system generates electricity and heat through a combined heat and power (CHP) unit and achieves electricity-to-heat conversion through a gas boiler (GB). Wind power (WT), a gas turbine (GT), and the upper power grid jointly supply electricity to electricity users. In addition, hydrogen energy is generated through electrolysis cells to supply hydrogen loads. Electric energy storage (ESS), thermal energy storage (TES), and hydrogen storage tanks (HES) can store or release energy when there is excess or insufficient energy.

[0206] (2) Thermal system modeling

[0207] Unlike the power system, which has low inertia and fast regulation, the thermal system has large system inertia during scheduling. The heat loss during the heat medium transmission process has a direct impact on the temperature of the heat medium at various locations. In the heat loss processing, the pipeline heat transmission loss caused by the temperature difference between the pipeline and the surrounding environment is calculated to obtain the pipeline end temperature.

[0208] (3) Integrated energy system optimization scheduling model

[0209] A two-way demand response mechanism of supply and demand is introduced, with the minimization of the comprehensive cost of system economy and wind curtailment penalty and the maximization of energy satisfaction as the optimization objectives. Considering the system energy balance constraints, equipment model constraints, system network constraints, etc., a multi-energy system optimization scheduling method taking into account the two-way demand response of supply and demand is proposed.

[0210] The process of formulating the optimal dispatching method of multi-energy system considering the supply and demand bidirectional demand response is as follows: Figure 1 The principles and steps are as follows:

[0211] 1) Initialization 101: Initialize basic parameters such as network structure, device access location, and maximum power;

[0212] 2) Obtaining day-ahead forecast data 102 of wind power and load;

[0213] 3) Modeling of integrated energy systems considering electricity-heat multi-energy flows103;

[0214] 4) Constructing a comprehensive demand response model for energy consumption that considers both electrical and thermal loads 104;

[0215] 5) Constructing a supply-side demand response model considering wind power, hydrogen production, and hydrogen storage tanks105;

[0216] 6) constructing a comprehensive objective function 106;

[0217] 7) Constructing constraints 107 for energy balance constraints, system network constraints, and device model constraints;

[0218] 8) Constructing the optimization problem 108;

[0219] 9) Solve the optimization problem 109;

[0220] 10) Output the day-ahead optimized dispatching plan 110 for the integrated energy system.

[0221] Example:

[0222] The multi-energy system optimization scheduling method proposed in this example, which takes into account the supply and demand bidirectional demand response, is based on a modified IEEE 33-node distribution system and a 6-node thermal system to form a comprehensive energy system example. Figure 2 The system includes cogeneration units, wind turbines, and gas turbine generator sets. The heat network converts energy with the power grid through cogeneration units and gas boilers. The system's electrical load, thermal load, hydrogen load, and wind power output curves are shown in Figure 1. Figure 3 The main parameters of each device are shown in Table 1.

[0223] Table 1 Equipment parameters

[0224]

[0225] This solution was modeled using the YALMIP toolbox and solved using Cplex 12.8.0. A comparative simulation analysis was conducted for the six scenarios described above, as shown in Table 2. In the table, "√" and "×" indicate whether the influencing factor was considered or not, respectively.

[0226] Table 2 Comparison of conditions of various scheduling schemes

[0227]

[0228] Figure 4 Considering demand response for electric loads before and after comparison, Figure 5 Comparison before and after considering demand response for heat load. The integrated demand response for electricity and heat on the energy consumption side achieves peak shaving and valley filling for both electricity and heat loads. Figure 6 The figure shows the comparison of wind power consumption in the system under six scenarios. Considering the demand response of electric load, thermal load and supply side, the wind power consumption ratio of the integrated energy system can be improved to varying degrees.

[0229] Figure 8 is the result of dispatching power balance optimization, Figure 10It is the result of optimizing the dispatching hydrogen energy balance. During the valley period and normal period of electricity price, wind power will be given priority to meet the hydrogen load demand through hydrogen production, and a part of the hydrogen energy will be stored in the hydrogen storage tank. The other part of wind power will supply the electric load, and the shortfall will be supplied by low-priced electricity. During the peak period of electricity price, the proportion of wind power consumed by the electric load will be increased, thereby reducing the amount of electricity purchased. The proportion of wind power consumed by hydrogen production will be reduced, and the shortfall will be released by the hydrogen storage tank.

[0230] Table 3 shows the cost comparison of each scheduling scheme. As can be seen from Table 3, by comparing the operating cost, wind curtailment penalty cost, hydrogen purchase cost and comprehensive target cost of each scheduling scheme:

[0231] Table 3 Comparison of costs of various scheduling schemes

[0232] Scenario Running cost / $ Wind curtailment penalty cost / $ Hydrogen purchase cost / $ Comprehensive target cost / $ 1 50421.8 3198.5 36759.2 90379.5 2 49791.1 2766.4 36759.2 89316.7 3 50201.8 3198.4 36759.2 90159.4 4 49571.3 2766.3 36759.2 89096.8 5 50666.7 3.8e-05 0 50666.7 6 49808.6 0.62 0 49809.2

[0233] 1) Compared with Scenario 1, Scenario 2 considers the response to the demand for electric load, shaving the peaks and filling the valleys of the demand curve for electric load, and reducing the total system cost by RMB 1,062.8; 2) Compared with Scenario 1, Scenario 3 considers the response to the demand for thermal load, shaving the peaks and filling the valleys of the demand curve for thermal load, and reducing the total system cost by RMB 220.1; 3) Compared with Scenario 1, Scenario 4 considers the comprehensive demand response for electricity and heat on the energy consumption side, and reduces the total cost by RMB 1,282.7. At the same time, the cost is lower than that when considering the demand response of a single type of load, and the total cost is reduced by RMB 219.9 and RMB 1,062.6 compared with Scenario 2 and Scenario 3 respectively; 4) Compared with Scenario 1, Scenario 5 considers the demand response on the energy supply side. The hydrogen load demand on the energy supply side during the valley period and the normal period is supplied by wind power; while during the peak period of electricity price, the hydrogen load is jointly borne by wind power and purchased hydrogen. As a result, the cost of purchasing hydrogen is greatly reduced, the level of wind power consumption is enhanced, the penalty cost for wind curtailment is reduced, and the total cost is reduced by 39,712.79996 yuan; 5) Compared with Scenario 1, Scenario 6 considers two-way demand response on the energy supply and consumption side, and the total cost is reduced by 40,570.28 yuan. At the same time, Scenario 6 reduces the total cost by 39,287.58 yuan compared to Scenario 4, which only considers comprehensive demand response on the energy consumption side, and reduces the total cost by 857.5 yuan compared to Scenario 5, which only considers demand response on the energy supply side. In summary, comprehensive demand response on the energy consumption side is more economical than single-type load demand response, and two-way demand response on the energy supply and consumption side can achieve better system economics than unidirectional demand response on the energy consumption side or the energy supply side.

[0234] In summary, the method proposed in the present invention flexibly adjusts the resources on the energy consumption side and the energy supply side, and better realizes multi-energy complementarity under the premise of ensuring energy demand, thereby achieving economic optimal scheduling. Due to the peak-shaving and valley-filling effect of demand response on the energy consumption side and the use of surplus wind power on the energy supply side for hydrogen production and conversion to hydrogen energy, the wind power absorption rate of the system will be improved. Therefore, the system's wind power access space is further increased. The multi-energy system optimization scheduling method proposed in the present invention, which takes into account the two-way demand response of supply and demand, is effective and reasonable.

[0235] Example 2

[0236] This embodiment provides a multi-energy system optimization and scheduling device that takes into account supply and demand bidirectional demand response, including:

[0237] Parameter acquisition module, used to determine and obtain basic operating parameters of the integrated energy system;

[0238] Integrated energy system model building module, used to build an integrated energy system model considering electricity, heat and other multi-energy flows;

[0239] The energy consumption side comprehensive demand response model construction module is used to construct the energy consumption side comprehensive demand response model considering the electrical load and thermal load;

[0240] Energy supply side demand response model construction module, used to build an energy supply side demand response model considering wind power, electric hydrogen production and hydrogen storage tanks;

[0241] Objective function construction module, used to construct the objective function of minimizing energy satisfaction and operating cost of the integrated energy system;

[0242] Constraint condition acquisition module, used to obtain the constraints of energy balance constraints, system network constraints, and device model constraints;

[0243] The objective function solving module is used to solve the objective function and obtain the day-ahead optimal scheduling plan for the integrated energy system.

[0244] A computing device comprising:

[0245] one or more processing units;

[0246] A storage unit for storing one or more programs,

[0247] In which, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the multi-energy system optimization scheduling method taking into account the two-way demand response of supply and demand as described above; it should be noted that the computing device may include but is not limited to a processing unit and a storage unit; those skilled in the art can understand that the computing device includes a processing unit and a storage unit and does not constitute a limitation on the computing device, and may include more components, or a combination of certain components, or different components. For example, the computing device may also include input and output devices, network access devices, buses, etc.

[0248] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of the multi-energy system optimization scheduling method taking into account the supply and demand bidirectional demand response are implemented as described above; it should be noted that the readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof; the program contained on the readable medium can be transmitted using any appropriate medium, including, but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof. For example, the program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a standalone software package, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0249] It should be understood that the embodiments and examples discussed herein are for illustrative purposes only and may be modified or altered by those skilled in the art, and all such modifications and alterations should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A multi-energy system optimization scheduling method taking into account supply and demand bidirectional demand response, characterized in that: The steps include: Determine and obtain basic operating parameters of the integrated energy system; Construct a comprehensive energy system model that considers electricity and heat multi-energy flows; Construct a comprehensive demand response model for energy consumption that takes into account both electrical and thermal loads; Construct a supply-side demand response model that considers wind power, hydrogen production, and hydrogen storage tanks; Construct an objective function to minimize energy satisfaction and operating costs of the integrated energy system; Obtain constraints for energy balance constraints, system network constraints, and device model constraints; Solve the objective function to obtain the day-ahead optimal dispatching plan for the integrated energy system; Constructing a comprehensive energy system model that considers electricity and heat multi-energy flows includes the following steps: Grid modeling for radial distribution network: Dist-Flow power flow equation is used to establish the network model of distribution network, including active power flow equation, reactive power flow equation and voltage equation, namely: Among them, P i,t and Q i,t are the active power and reactive power on the line from grid node i to node i+1 at time t; r i is the resistance of the line from node i to node i+1 at time t; and are the active load and power source active power at grid node i at time t; x i is the reactance of the line from node i to node i+1 at time t; and are the reactive load and power supply reactive power at grid node i at time t respectively; V i,t is the voltage of node i at time t; Introducing the variables shown in formula (4), performing second-order cone relaxation on the distribution network model, we get formula (5): Heat network modeling: The heat change relationship of pipeline ij can be expressed as: Where H i,t and H j,t are the heat energy flows at node i and node j at time t in the heat network system; L ij is the length of the pipe ij; ∑R unit is the thermal resistance per unit length from the heat medium to the ambient medium; T ij，t is the heat medium temperature of pipe ij at time t in the heat network system; T a,t is the ambient temperature of the pipe; c is the specific heat capacity of water; m i,t and m j,t are the heat medium mass flows at nodes i and j respectively; T i,t and T j,t are the heat medium temperatures at node i and node j respectively; The heat transfer loss in the pipeline due to the temperature difference between the pipeline and the surrounding environment, the temperature at the end of the pipeline can be expressed as: Among them, α ij is the loss constant of pipeline ij; k ij is the heat loss coefficient of pipeline ij; m ij，t is the heat medium mass flow in pipe ij at time t in the heat network system.

2. The multi-energy system optimization scheduling method considering supply and demand bidirectional demand response according to claim 1 is characterized in that: Constructing a comprehensive demand response model for energy consumption that considers both electrical and thermal loads involves the following steps: Electric load demand response modeling: in, and They represent the fixed load, transferable load and total load at grid node i at time t respectively; represents the upper limit of the transferable electric load at grid node i; represents the total amount of transferable electric load at grid node i during the dispatch period; Heat Load Demand Response Modeling: in, and They represent the fixed heat load, transferable heat load and total heat load at the node k of the heating network at time t respectively; represents the upper limit of the transferable heat load at the heating network node k; It represents the total amount of heat load that can be transferred at node k in the heating network during the scheduling period.

3. The multi-energy system optimization scheduling method considering supply and demand bidirectional demand response according to claim 2 is characterized in that: Constructing a supply-side demand response model that considers wind power, hydrogen production, and hydrogen storage tanks includes the following steps: Constructing electric hydrogen production and hydrogen storage tank models: I ch,t,i +I dis,t,i ≤1 (21) in, and H 2t,i are the power consumption and hydrogen production power of the electrolyzer at grid node i at time t; η i represents the hydrogen production efficiency of the electrolyzer at grid node i; represents the maximum power consumption of the electrolyzer at grid node i; and are the maximum power of hydrogen filling and hydrogen discharge of the hydrogen storage tank at grid node i, respectively; and are the power of hydrogen filling and hydrogen discharging at the hydrogen storage tank at grid node i at time t, respectively; and are the hydrogen storage capacity of the hydrogen storage tank at grid node i at time t-1 and t respectively; are the minimum and maximum hydrogen storage capacities of the hydrogen storage tank at grid node i; α p2h,i is the loss rate of the hydrogen storage tank at grid node i; η ch,i and η dis,i are the hydrogen energy storage efficiency and hydrogen energy release efficiency of the hydrogen storage tank at grid node i, respectively; I ch,t,i and I dis,t,i is a 0-1 variable indicating whether the hydrogen storage tank at grid node i is in energy storage or release state at time t; Wind power dispatch constraint formulation: in, represents the predicted wind power at grid node i at time t; represents the actual wind power at grid node i at time t, and They represent the wind power parts participating in hydrogen production and system dispatch at grid node i at time t respectively.

4. The multi-energy system optimization scheduling method considering supply and demand bidirectional demand response according to claim 3 is characterized in that: Constructing the objective function of energy satisfaction and minimum operating cost of the integrated energy system includes the following steps: Operation cost objective function formulation: For the types of units involved, operating costs include equipment energy supply costs and electricity purchase and sales costs: Among them, C energy The cost of supplying energy to the system, is the equipment operating cost of unit n during period t, where m3 = {GT, GB, CHP, WT, ESS, TES, HES}, where GT, GB, WT, CHP, ESS, TES, and HES are gas turbines, gas boilers, wind power, combined heat and power units, electric energy storage, heat storage tanks, and hydrogen storage tanks, respectively; N m3 is the total number of energy supply units; is the cost of electricity purchase and sale in period t; is the cost of purchasing hydrogen during period t; in, are the purchased power and sold power from the upper power grid during period t, are the electricity purchase price and electricity sales price at time t respectively; Among them, p t ,λ t are the hydrogen purchasing power and hydrogen purchasing price in period t respectively; Setting of penalty costs for wind curtailment: Among them, C punis represents the penalty cost for wind curtailment; c wt represents the wind curtailment penalty coefficient; User energy satisfaction objective function formulation: Among them, C dr represents the penalty cost for wind curtailment; v e , α e is the electricity preference coefficient, v h , α h is the heat preference coefficient; Comprehensive optimization objective function formulation: minF=min(C energy +C punis +C dr ) (30) Among them, F is the comprehensive cost of the system.

5. A multi-energy system optimization scheduling method taking into account supply and demand bidirectional demand response according to claim 4, characterized in that: The equipment model constraints include upper and lower limit constraints on unit output, unit ramp constraints, energy storage equipment constraints, and energy conversion constraints on extraction-condensing thermal power units.

6. The multi-energy system optimization scheduling method considering supply and demand bidirectional demand response according to claim 5 is characterized in that: The commercial solver CPLEX is used to solve the integrated energy system model.

7. A multi-energy system optimization and dispatching device taking into account supply and demand bidirectional demand response, characterized by: include: Parameter acquisition module, used to determine and obtain basic operating parameters of the integrated energy system; Integrated energy system model building module, used to build an integrated energy system model considering electricity, heat and other multi-energy flows; The energy consumption side comprehensive demand response model construction module is used to construct the energy consumption side comprehensive demand response model considering the electrical load and thermal load; Energy supply side demand response model construction module, used to build an energy supply side demand response model considering wind power, electric hydrogen production and hydrogen storage tanks; Objective function construction module, used to construct the objective function of minimizing energy satisfaction and operating cost of the integrated energy system; Constraint condition acquisition module, used to obtain the constraints of energy balance constraints, system network constraints, and device model constraints; The objective function solving module is used to solve the objective function and obtain the day-ahead optimal scheduling plan for the integrated energy system; Constructing a comprehensive energy system model that considers electricity and heat multi-energy flows includes the following steps: Grid modeling for radial distribution network: Dist-Flow power flow equation is used to establish the network model of distribution network, including active power flow equation, reactive power flow equation and voltage equation, namely: Among them, P i,t and Q i,t are the active power and reactive power on the line from grid node i to node i+1 at time t; r i is the resistance of the line from node i to node i+1 at time t; and are the active load and power source active power at grid node i at time t; x i is the reactance of the line from node i to node i+1 at time t; and are the reactive load and power supply reactive power at grid node i at time t respectively; V i,t is the voltage of node i at time t; Introducing the variables shown in formula (4), performing second-order cone relaxation on the distribution network model, we get formula (5): Heat network modeling: The heat change relationship of pipeline ij can be expressed as: Where H i,t and H j,t are the heat energy flows at node i and node j at time t in the heat network system; L ij is the length of the pipe ij; ∑R unit is the thermal resistance per unit length from the heat medium to the ambient medium; T ij，t is the heat medium temperature of pipe ij at time t in the heat network system; T a,t is the ambient temperature of the pipe; c is the specific heat capacity of water; m i,t and m j,t are the heat medium mass flows at nodes i and j respectively; T i,t and T j,t are the heat medium temperatures at node i and node j respectively; The heat transfer loss in the pipeline due to the temperature difference between the pipeline and the surrounding environment, the temperature at the end of the pipeline can be expressed as: Among them, α ij is the loss constant of pipeline ij; k ij is the heat loss coefficient of pipeline ij; m ij，t is the heat medium mass flow in pipe ij at time t in the heat network system.

8. A computing device, characterized in that: include: one or more processing units; A storage unit for storing one or more programs, wherein when the one or more programs are executed by the one or more processing units, the one or more processing units execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a non-volatile program code executable by a processor, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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