Optimal scheduling method for regional multi-energy complementary energy system based on source-load uncertainty

Abstract

The present invention discloses a method for optimizing the scheduling of a regional multi-energy complementary energy system based on source-load uncertainty, comprising: 1. determining the main equipment of the regional multi-energy complementary energy system based on source-load uncertainty and constructing a system structure diagram; 2. establishing mathematical models of wind power, photovoltaics, batteries, gas turbines, ORC waste heat recovery devices, and heat storage tanks, as well as power balance constraint equations for the internal power grid and heat network; 3. taking the minimum overall carbon emissions of the system as the goal, taking into account the interaction between integrated energy stations in different regions, and considering the optimal scheduling of the regional multi-energy complementary energy system based on source-load uncertainty. The present invention adopts information gap decision theory to deal with load-end uncertainty, which can more reasonably schedule the regional multi-energy complementary energy system, thereby improving the system's anti-interference ability and reducing the system's overall carbon emissions.

Description

Technical Field

[0001] The present invention relates to the field of regional multi-energy complementary energy system optimization scheduling, in particular to a regional multi-energy complementary energy system optimization scheduling based on source-load uncertainty. Background Art

[0002] With the development of the global economy, energy consumption continues to grow, bringing with it more severe environmental pollution problems. This problem has become a global challenge facing humanity. Against the backdrop of the vigorous promotion of the dual-carbon policy, promoting energy transformation and building a green, low-carbon, safe, and clean energy system dominated by renewable energy is a strategic choice for contemporary society. The proportion of renewable energy sources such as wind and solar energy in primary energy is central to this energy transformation. However, due to the uncertainty and randomness of renewable energy output, serious wind and solar power curtailment has occurred, posing a significant challenge to system stability. As a key component of the low-carbon transition, regional multi-energy complementary energy systems must be robustly optimized to improve energy efficiency and reduce overall carbon emissions, based on the uncertainty of wind and solar power output.

[0003] At present, the optimal scheduling of regional multi-energy complementary energy systems mainly focuses on the energy control strategy of distributed power sources and energy storage within a specific system. Although some studies have considered the impact of interactions between different systems on the optimal scheduling of the system, they mainly focus on this specific system and study the impact of the interaction between this system and the outside world on the internal scheduling of this system. They rarely consider the impact of equal interactions between multiple different regional systems. At the same time, the uncertainty of source and load, that is, the robustness of the source end and the uncertainty of the load end, are also rarely used in this scenario. Summary of the Invention

[0004] In order to promote the transformation of regional multi-energy complementary energy systems to high efficiency and low carbon, the present invention provides a regional multi-energy complementary energy system optimization scheduling method based on source-load uncertainty, so as to more reasonably realize the high efficiency of energy utilization in the regional multi-energy complementary energy system through the study of the robustness of wind and solar power output and the uncertainty of load, reduce the comprehensive carbon emissions of the regional multi-energy complementary energy system and improve the energy utilization rate, so that the regional multi-integrated energy station system is in an efficient and low-carbon operation state.

[0005] To achieve the above object, the present invention adopts the following technologies:

[0006] The present invention provides an optimization scheduling method for a regional multi-energy complementary energy system based on source-load uncertainty. The regional multi-energy complementary energy system is composed of several integrated energy stations, a superior power grid, and external natural gas. Each integrated energy station is composed of an internal power grid and a heat network, and includes: wind power (WT), photovoltaic (PV), battery (ES), gas turbine (GT), ORC waste heat recovery device, and heat storage tank (TE).

[0007] The upper power grid and wind power and photovoltaic power are used to provide electric energy to the internal power grid of the integrated energy station, and the internal power grid of the integrated energy station provides electric energy to the electric load and the electric vehicle load;

[0008] The external natural gas grid provides natural gas to the gas turbine, and the gas turbine generates electricity and heat by burning the natural gas, wherein the generated electricity is provided to the internal power grid of the integrated energy station, and the generated heat is provided to the ORC waste heat recovery device and the heat storage tank;

[0009] The ORC waste heat recovery device generates electrical energy by absorbing thermal energy and supplies it to the internal power grid of the integrated energy station;

[0010] The heat storage tank is used to store or release thermal energy;

[0011] The storage battery is used to store or release electrical energy;

[0012] Its characteristic is that the regional multi-energy complementary energy system optimization scheduling method includes the following steps:

[0013] Step 1: Considering the working status of the regional multi-energy complementary energy system under wind and solar power output at different time periods, a mathematical model of the regional multi-energy complementary energy system is established, including: mathematical models of wind power WT, photovoltaic PV, battery ES, gas turbine GT, ORC waste heat recovery device, heat storage tank TE and integrated energy station;

[0014] Step 2: Construct a robust optimization model for the source side of the regional multi-energy complementary energy system

[0015] Step 2.1, use formula (1) to obtain the mathematical model for handling uncertainty of wind and solar power;

[0016] (1)

[0017] In formula (1), Represents a set of uncertain parameters; Represents the uncertain elements in the mth row of the wind parameter matrix when the mathematical model constraints of wind power are abstracted into a matrix form. Represents the uncertain element in the mth row and nth column of the wind and solar parameter matrix; Indicates the uncertain element in row m and column n Randomly fluctuating parameter values; Indicates the uncertain element in row m and column n The fluctuation deviation value of Indicates the uncertain element in the mth row The number of represents the robust risk indicator of the mth row;

[0018] Step 2.2: Use Equations (2) and (3) to obtain the robust equivalent model of wind and solar power.

[0019] (2)

[0020] (3)

[0021] In formula (2), 、 are the actual output and maximum output of photovoltaic pv at time t respectively; 、 is the actual output and maximum output of wind turbine wt at time t;

[0022] In formula (3), 、 and Both represent auxiliary variables introduced to deal with the uncertainty of photovoltaic power (PV) or wind power (WT); is the fluctuation amplitude of photovoltaic power pv or wind power wt at time t;

[0023] Step 3: Use equations (4) and (5) to construct a universal optimization model of the load-side IGDT of the regional multi-energy complementary energy system:

[0024] (4)

[0025] In formula (4), is the coefficient of deviation of the uncertainty; is the maximum value of the objective function; is the benchmark value of the objective function; is the avoidance coefficient; 、 are equality constraints and inequality constraints respectively; is the objective function of the model; is the decision variable, is the decision variable The optimal value of is an uncertain quantity, Uncertain quantity The predicted value of is a set of uncertain quantities, and:

[0026] (5)

[0027] In formula (5), is the load forecast value of area q at time t; is the load value of area q at time t; is the set of uncertainties in region q at time t;

[0028] Step 4, using formula (6) to construct a mathematical model of comprehensive carbon emissions of the regional multi-energy complementary energy system;

[0029] (6)

[0030] In formula (6), is the carbon emissions generated by the i-th integrated energy station at time t; is the comprehensive carbon emissions of the regional multi-energy complementary energy system;

[0031] Step 5: Use formula (7) to construct a mathematical model of power balance constraints between the integrated energy stations in the regional multi-energy complementary energy system:

[0032] (7)

[0033] In formula (7), 、 are the electric energy purchased and sold by the i-th and j-th integrated energy stations from other integrated energy stations at time t, respectively; j∈[1,Y];

[0034] Step 6: Construct a mathematical model of power balance constraints for each integrated energy station in the regional multi-energy complementary energy system.

[0035] Step 6.1: Use equation (8) to obtain the power balance constraint of the internal power grid of the integrated energy station at time t:

[0036] (8)

[0037] In formula (8), is the electric power output by the gas turbine gt at time t; is the purchased electricity at time t; is the interaction power with the battery es at time t; is the electric energy generated by the ORC waste heat recovery device at time t; 、 are the electric vehicle load and electricity load of the integrated energy station at time t respectively;

[0038] Step 6.2: Use equation (9) to obtain the power balance constraint of the heating network of the integrated energy station at time t:

[0039] (9)

[0040] In formula (9), and are the thermal energy charging and discharging powers of the heat storage tank te at time t respectively; is the output thermal power of the gas turbine gt at time t; is the waste heat power input to the ORC waste heat power generation device at time t;

[0041] Step 7: Comprehensive carbon emissions of regional multi-energy complementary energy systems The minimum is used as the objective function, and together with the mathematical model of the regional multi-energy complementary energy system, the power balance constraints between the integrated energy stations, and the power balance constraints of the internal power grid and heat network of each integrated energy station, the overall model of the regional multi-energy complementary energy system is formed;

[0042] Step 8: Use the branch and bound method to solve the overall model of the regional multi-energy complementary energy system to obtain a robust optimization scheduling method.

[0043] The robust optimization scheduling method for a regional multi-energy complementary energy system based on wind and solar power output uncertainty according to the present invention is also characterized in that step 1 comprises:

[0044] Step 1.1, establish the mathematical model of the storage battery es and the heat storage tank te;

[0045] Step 1.1.1, use equations (10) and (11) to establish the mathematical model of the battery es:

[0046] (10)

[0047] In formula (10), 、 is the real-time power of the battery es at time t and time t-1; is the self-consumption coefficient of the storage battery es; 、 is the charge and discharge power of the battery es at time t; 、 is the charge and discharge efficiency of the battery es at time t;

[0048] (11)

[0049] In formula (11), 、 is the minimum and maximum capacity factor of the storage battery es; is the rated capacity of the storage battery es; 、 is the charge of the battery es at the initial and final moments; 、 is the charge and discharge state of the battery es at time t; is the power capacity ratio of the storage battery es;

[0050] Step 1.1.2, use equations (12) and (13) to establish the mathematical model of the heat storage tank te:

[0051] (12)

[0052] In formula (12), and are the thermal energy capacity stored in the heat storage tank te at time t and time t-1 respectively; is the self-loss coefficient of the heat storage tank te; and are the heat energy charging and discharging efficiencies of the heat storage tank te respectively; and are the thermal energy charging and discharging powers of the heat storage tank te at time t respectively; is the time step;

[0053] (13)

[0054] In formula (13), is the state variable representing the charging and heating operation state of the heat storage tank te at time t; is the total capacity of the heat storage tank te; 、 are the capacities of the heat storage tank te at the initial and final moments respectively;

[0055] Step 1.2, establish mathematical models of photovoltaic (PV), wind power (WT), gas turbine (GT), and ORC waste heat recovery device;

[0056] Step 1.2.1, use formula (14) to establish the mathematical model of photovoltaic PV:

[0057] (14)

[0058] In formula (14), is the rated power of photovoltaic pv at time t; is the external irradiance at time t; is the standard irradiance; is the power temperature coefficient of photovoltaic pv; is the actual operating temperature of photovoltaic pv at time t; is the standard operating temperature of photovoltaic pv;

[0059] Step 1.2.2: Use formula (15) to establish the mathematical model of wind power wt:

[0060] (15)

[0061] In formula (15), is the rated power of wind power wt; is the wind speed at time t; is the cut-in wind speed of wind power wt; is the wind speed at rated power; is the cut-out wind speed of wind power wt;

[0062] Step 1.2.3: Use Equations (16) to (18) to obtain the mathematical model of the gas turbine gt:

[0063] (16)

[0064] (17)

[0065] In formula (16), 、 are the natural gas power consumed and the electric power generated by the gas turbine gt at time t, respectively; is the power generation efficiency of the gas turbine gt;

[0066] In formula (17), is the thermal power output of the gas turbine gt at time t; is the thermal-to-electricity ratio of the gas turbine gt;

[0067] (18)

[0068] In formula (18), Indicates the minimum load rate of the gas turbine gt; is the capacity of the gas turbine gt;

[0069] In step 1.2.4, the mathematical model of the ORC waste heat recovery device is obtained using equations (19) and (20):

[0070] (19)

[0071] In formula (19), 、 are the power generation power and input waste heat power of the ORC waste heat power generation device at time t respectively; The power generation efficiency of the waste heat power generation device;

[0072] (20)

[0073] In formula (20), 、 are the minimum and maximum values ​​of the waste heat power input to the ORC waste heat power generation device at time t, respectively; and are the minimum and maximum values ​​of the ramp power of the ORC waste heat power generation device respectively;

[0074] Step 1.3: Establish a mathematical model of carbon emissions from integrated energy stations using equations (21) to (23);

[0075] (twenty one)

[0076] In formula (21), and are the standard coal conversion coefficients of electric energy e and natural gas gas respectively; is the carbon emission coefficient of complete combustion of standard coal;

[0077] (twenty two)

[0078] In formula (22), 、 are the power purchased by any integrated energy station at time t and the loss incurred during the transmission of power by the integrated energy station; and represents the electricity sales volume and electric vehicle load of any integrated energy station at time t; is the transmission efficiency coefficient of any integrated energy station;

[0079] (twenty three)

[0080] In formula (23), is the actual output of the gas turbine at time t.

[0081] The electronic device of the present invention includes a memory and a processor, and is characterized in that the memory is used to store a program that supports the processor to execute the regional multi-energy complementary energy system optimization scheduling method, and the processor is configured to execute the program stored in the memory.

[0082] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which is characterized in that when the computer program is run by a processor, the steps of the regional multi-energy complementary energy system optimization scheduling method are executed.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] 1. The present invention proposes an optimized scheduling structure for a regional multi-energy complementary energy system based on source-load uncertainty, which includes photovoltaics, wind power units, gas turbines, batteries, heat storage tanks, ORC waste heat recovery devices, and integrated energy station systems. Against the backdrop of the dual carbon goals, and considering the impact of uncertainty in wind and solar renewable energy output, a robust optimized scheduling method for a regional multi-energy complementary energy system based on wind and solar output uncertainty is proposed. By studying the robustness of wind and solar output, the optimized scheduling of the regional multi-energy complementary energy system is affected, thereby improving the system's operating efficiency and reducing carbon emissions. While ensuring safe and reliable regional operation, the rational flow of energy within the regional multi-energy complementary energy system is achieved.

[0085] 2. The collaborative scheduling optimization method of the present invention expands the coverage of the energy system and fully utilizes the information transmission and energy interaction between multiple integrated energy stations in the multi-energy complementary energy system, achieving a reasonable impact on the optimized scheduling of the regional multi-energy complementary energy system. Based on the uncertainty characteristics of load, the flexible operation of the regional multi-energy complementary energy system is achieved during different periods of wind and solar power output. Through the coordinated scheduling of integrated energy stations, the flow of electric energy is realized. On the basis of meeting load requirements, the energy utilization rate of the system is effectively improved. Specifically, while considering reducing the overall carbon emissions of the system, the system exergy efficiency is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 This is a structural diagram of a regional multi-energy complementary energy system based on the uncertainty of wind and solar power output according to the present invention;

[0087] Figure 2 This is a structural diagram of a comprehensive energy station in a regional multi-energy complementary energy system based on the uncertainty of wind and solar power output according to the present invention;

[0088] Figure 3 This is a flow chart of the robust optimization scheduling method for a regional multi-energy complementary energy system based on the uncertainty of wind and solar power output according to the present invention. DETAILED DESCRIPTION

[0089] In this embodiment, Figure 3 As shown in the figure, a regional multi-energy complementary energy system optimization scheduling method based on source-load uncertainty is proposed to address the uncertainty problem of source and load at different times. It is proposed to use robust linear optimization theory to deal with the uncertainty at the source end, and use information gap decision theory (IGDT) to deal with the uncertainty at the load end, so as to schedule the regional multi-energy complementary energy system more reasonably.

[0090] like Figure 1The system shown is a regional multi-energy complementary energy system based on source-load uncertainty, which consists of four integrated energy station interaction frameworks, a superior power grid, and an external natural gas network. Each integrated energy station consists of an internal power grid and a heat network, and includes: wind power, photovoltaics, batteries, gas turbines, ORC waste heat recovery devices, and heat storage tanks. The structural diagram of each integrated energy station is shown in the figure below. Figure 2 As shown;

[0091] The system uses integrated energy stations as the main body to participate in regional coordinated dispatching. The upper power grid and external gas grid supply energy to the integrated energy stations. When the integrated energy stations carry out coordinated dispatching, they transmit information to each other and realize the flow of electric energy.

[0092] The integrated energy station is powered by electricity from the upstream power grid, wind power, and photovoltaic power generation. The external gas grid provides natural gas to the gas turbines to generate electricity and heat. All of this electricity is used to supply electricity load users and electric vehicle users in the area.

[0093] Batteries are used to store or release electrical energy to regulate energy flow;

[0094] Thermal storage tanks are used to store or release thermal energy;

[0095] The ORC waste heat recovery device generates electricity by absorbing excess heat energy and supplies it to the internal power grid;

[0096] The method comprises the following steps:

[0097] Step 1: Considering the working state of the regional multi-energy complementary energy system under the uncertainty of wind and solar power output and load at different time periods, a mathematical model of the regional multi-energy complementary energy system is established, including: mathematical models of wind power WT, photovoltaic PV, battery ES, gas turbine GT, ORC waste heat recovery device, heat storage tank TE and integrated energy station;

[0098] Step 1.1, establish the mathematical model of the storage battery es and the heat storage tank te;

[0099] Step 1.1.1, use equations (1) and (2) to establish the mathematical model of the battery es:

[0100] (1)

[0101] In formula (1), 、 is the real-time power of the battery es at time t and time t-1; is the self-consumption coefficient of the storage battery es, which is generally 1%; 、 is the charge and discharge power of the battery es at time t; 、 is the charge and discharge efficiency of the storage battery es at time t, which is mainly related to factors such as battery type and battery life;

[0102] (2)

[0103] In formula (2), 、 are the minimum and maximum capacity factors of the storage battery es, usually 10% and 90%; is the rated capacity of the storage battery es; 、 The initial and final charge of the battery es means that the battery's charge must be equal at the beginning and end of a cycle. 、 is the charge and discharge state of the battery es at time t, which is a 0-1 variable; is the power capacity ratio of the storage battery es;

[0104] Step 1.1.2, use equations (3) and (4) to establish the mathematical model of the heat storage tank te:

[0105] (3)

[0106] In formula (3), and are the thermal energy capacity stored in the heat storage tank te at time t and time t-1 respectively; is the self-loss coefficient of the heat storage tank te; and are the heat energy charging and discharging efficiencies of the heat storage tank te respectively; and are the thermal energy charging and discharging powers of the heat storage tank te at time t respectively; is the time step, in this embodiment, it is 1h;

[0107] (4)

[0108] In formula (4), is the state variable representing the charging and heating operation state of the heat storage tank te at time t; is the total capacity of the heat storage tank te; 、 are the capacities of the heat storage tank te at the initial and final moments, respectively, which means that the initial and final charges of the heat storage tank te must be equal within a cycle.

[0109] Step 1.2, establish mathematical models of photovoltaic (PV), wind power (WT), gas turbine (GT), and ORC waste heat recovery device;

[0110] Step 1.2.1, use formula (5) to establish the mathematical model of photovoltaic PV:

[0111] (5)

[0112] In formula (5), is the rated power of photovoltaic pv at time t; is the external irradiance at time t, which is related to various environmental factors such as weather and temperature; is the standard irradiance; is the power temperature coefficient of photovoltaic pv; is the actual operating temperature of photovoltaic pv at time t; It is the standard operating temperature of photovoltaic PV.

[0113] Step 1.2.2: Use equation (6) to establish the mathematical model of wind power wt:

[0114] (6)

[0115] In formula (6), is the rated power of wind power wt; is the wind speed at time t; is the cut-in wind speed of wind power wt, that is, the wind speed at which wind power starts to work; is the wind speed at rated power; The cut-out wind speed of wind turbine wt is the wind speed at which wind turbine operates at maximum power.

[0116] Step 1.2.3: Use Equations (7) to (9) to obtain the mathematical model of the gas turbine gt:

[0117] (7)

[0118] (8)

[0119] In formula (7), 、 are the natural gas power consumed and the electric power generated by the gas turbine gt at time t, respectively; is the power generation efficiency of the gas turbine gt;

[0120] In formula (8), is the thermal power output of the gas turbine gt at time t; is the thermal-to-electricity ratio of the gas turbine gt, usually taken as 55%;

[0121] (9)

[0122] In formula (9), Indicates the minimum load rate of the gas turbine gt; is the capacity of the gas turbine gt;

[0123] In step 1.2.4, the mathematical model of the ORC waste heat recovery device is obtained using equations (10) and (11):

[0124] (10)

[0125] In formula (10), 、 are the power generation power and input waste heat power of the ORC waste heat power generation device at time t respectively; The power generation efficiency of the waste heat power generation device;

[0126] (11)

[0127] In formula (11), 、 are the minimum and maximum values ​​of the waste heat power input to the ORC waste heat power generation device at time t, respectively; and are the minimum and maximum values ​​of the ramp power of the ORC waste heat power generation device respectively.

[0128] Step 1.3: Establish a mathematical model of carbon emissions from integrated energy stations using equations (12) to (14);

[0129] (12)

[0130] In formula (12), and The standard coal conversion coefficients of electric energy e and natural gas gas are 0.1229 kg standard coal / kWh and 1.10-1.33 kg standard coal / cubic meter respectively; is the carbon emission coefficient of complete combustion of standard coal, generally 0.67;

[0131] (13)

[0132] In formula (13), 、 are the electricity purchased by any integrated energy station at time t and the losses incurred during the transmission of electricity by the integrated energy station; and represents the electricity sales volume and electric vehicle load of any integrated energy station at time t; is the transmission efficiency coefficient of any integrated energy station, which is related to the technical level of the integrated energy station;

[0133] (14)

[0134] In formula (14), is the actual output of the gas turbine gt at time t.

[0135] Step 2: constructing a source-measurement robust optimization model for the regional multi-energy complementary energy system;

[0136] Step 2.1, use formula (15) to obtain the mathematical model for handling uncertainty of wind and solar power;

[0137] (15)

[0138] In formula (15), Represents a set of uncertain parameters; Represents the uncertain elements in the mth row of the wind parameter matrix when the mathematical model constraints of wind power are abstracted into a matrix form. Represents the uncertain element in the mth row and nth column of the wind and solar parameter matrix; Indicates the uncertain element in row m and column n The parameter value of random fluctuations; Indicates the uncertain element in row m and column n The fluctuation deviation value of Indicates the uncertain parameters of the mth row The number of Represents the robust risk indicator of the mth row, which determines the fluctuation range of the parameter. When the value is the maximum, it means that the fluctuation value changes to the maximum. A value of zero indicates a deterministic model.

[0139] Step 2.2: Use Equations (16) and (17) to obtain the robust equivalent mathematical model of wind and solar power.

[0140] (16)

[0141] (17)

[0142] In formula (16), 、 are the actual output and maximum output of photovoltaic pv at time t respectively; 、 is the actual output and maximum output of wind power wt at time t;

[0143] In formula (17), 、 and They all represent auxiliary variables introduced to deal with the uncertainty of photovoltaic pv or wind power wt and have no meaning; is the fluctuation amplitude of photovoltaic power pv or wind power wt at time t; .

[0144] Step 3: Use equations (18) and (19) to construct the load-side IGDT general optimization model of the regional multi-energy complementary energy system:

[0145] (18)

[0146] Formula (18), is the coefficient of deviation of the uncertainty; is the maximum value of the objective function; is the benchmark value of the objective function, which is usually the objective function value when the uncertainty is the predicted value; is the avoidance coefficient, which represents the decision maker's ability to accept additional investment; 、 are equality constraints and inequality constraints respectively; is the objective function of the model; 、 are the optimal value of decision variables and decision variables respectively; 、 are the predicted value and the uncertainty quantity respectively; is a set of uncertain quantities, and:

[0147] (19)

[0148] Formula (19), is the load forecast value of area q at time t; is the load value of area q at time t; is the set of uncertainties in region q at time t.

[0149] Step 4, using formula (20) to construct a mathematical model of comprehensive carbon emissions of the regional multi-energy complementary energy system;

[0150] (20)

[0151] In formula (20), is the carbon emissions generated by the i-th integrated energy station at time t; It is the comprehensive carbon emissions of the regional multi-energy complementary energy system.

[0152] Step 5: Use formula (21) to construct a mathematical model of power balance constraints between the integrated energy stations in the regional multi-energy complementary energy system:

[0153] (twenty one)

[0154] In formula (21), 、 are the electric energy purchased and sold by the i-th and j-th integrated energy stations from other integrated energy stations at time t, respectively; j∈[1,Y].

[0155] Step 6: Construct a power balance constraint mathematical model for the regional multi-energy complementary energy system integrated energy station

[0156] Step 6.1: Use Equation (22) to obtain the power balance constraint of the internal power grid of the integrated energy station at time t:

[0157] (twenty two)

[0158] In formula (22), is the photovoltaic power output at time t; is the wind power output wt at time t; is the electric power output by the gas turbine gt at time t; is the interaction power with the battery es at time t; is the electric energy generated by the ORC waste heat recovery device at time t;

[0159] Step 6.2: Use Equation (23) to obtain the power balance constraint of the internal heat network of the integrated energy station at time t:

[0160] (twenty three)

[0161] In formula (23), and are the thermal energy charging and discharging powers of the heat storage tank te at time t respectively; is the output thermal power of the gas turbine gt at time t; is the waste heat power input by the ORC waste heat power generation device at time t.

[0162] Step 7: Comprehensive carbon emissions of regional multi-energy complementary energy systems Minimum as the objective function:

[0163] (twenty four)

[0164] In step 8, MATLAB is used to call the CPLEX solver, and the branch and bound method is used to solve the mixed integer linear programming model shown in formula (27) to obtain the collaborative scheduling optimization method.

[0165] (25)

[0166] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0167] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

[0168] In this embodiment, for the regional multi-energy complementary energy system based on source-load uncertainty, Figure 1 The structural construction example shown;

[0169] In order to compare and analyze the optimization method proposed in this invention on the carbon emissions of the optimized regional multi-energy complementary energy system, the following four optimization methods are set for the example scenario:

[0170] 1: Dispatching the regional multi-energy complementary energy system without considering the uncertainty of source and load, with the highest efficiency as the objective function;

[0171] 2: Dispatching the regional multi-energy complementary energy system without considering the uncertainty of source and load, with the lowest comprehensive carbon emissions as the objective function;

[0172] 3: Considering the uncertainty of source and load, the regional multi-energy complementary energy system is dispatched with the highest efficiency as the objective function;

[0173] 4: Considering the uncertainty of source and load, the regional multi-energy complementary energy system is dispatched, with the lowest comprehensive carbon emissions as the objective function;

[0174] After solving the model, we get the results shown in Table 1:

[0175] Table 1

[0176]

[0177] As shown in Table 1, when Method 1 does not consider the uncertainty of source and load for regional multi-energy complementary energy system scheduling and the objective function is not the lowest comprehensive carbon emissions, the comprehensive carbon emissions of this regional multi-energy complementary energy system are the highest; by comparing Method 1 with Method 2, it can be seen that when the regional multi-energy complementary energy system scheduling is performed without considering the uncertainty of source and load, when the objective function is changed to comprehensive carbon emissions, the comprehensive carbon emissions of the system will decrease, but the degree of reduction is not large; by comparing Method 1 with Method 3, it can be seen that when the regional multi-energy complementary energy system scheduling is performed with the uncertainty of source and load considered, and the comprehensive carbon emissions of the system are not used as the objective function, the comprehensive carbon emissions of the system will decrease. The degree of reduction in carbon emissions is relatively large; by comparing Method 2 with Method 3, it can be seen that for the same regional multi-energy complementary energy system, one considers source-load uncertainty to schedule the regional multi-energy complementary energy system, and the other uses the system's comprehensive carbon emissions as the objective function. Relatively speaking, the impact of considering source-load uncertainty on the system's comprehensive efficiency is greater than the objective function; by comparing Method 1, Method 2, Method 3 with Method 4, it can be seen that when the source-load uncertainty considered in the present invention is used to schedule the regional multi-energy complementary energy system and the system's comprehensive carbon emissions are used as the objective function at the same time, the comprehensive carbon emissions of this regional multi-energy complementary energy system will be significantly reduced.

Claims

1. A method for optimizing the scheduling of a regional multi-energy complementary energy system based on source-load uncertainty, wherein the regional multi-energy complementary energy system consists of several integrated energy stations, a superior power grid, and external natural gas; Each integrated energy station consists of an internal power grid and a heat network, and includes: wind power wt, photovoltaic PV, battery es, gas turbine gt, ORC waste heat recovery device, and heat storage tank te; The upper power grid and wind power and photovoltaic power are used to provide electric energy to the internal power grid of the integrated energy station, and the internal power grid of the integrated energy station provides electric energy to the electric load and the electric vehicle load; The external natural gas grid provides natural gas to the gas turbine, and the gas turbine generates electricity and heat by burning the natural gas, wherein the generated electricity is provided to the internal power grid of the integrated energy station, and the generated heat is provided to the ORC waste heat recovery device and the heat storage tank; The ORC waste heat recovery device generates electrical energy by absorbing thermal energy and supplies it to the internal power grid of the integrated energy station; The heat storage tank is used to store or release thermal energy; The storage battery is used to store or release electrical energy; It is characterized in that the regional multi-energy complementary energy system optimization scheduling method includes the following steps: Step 1: Considering the working status of the regional multi-energy complementary energy system under wind and solar power output at different time periods, a mathematical model of the regional multi-energy complementary energy system is established, including: mathematical models of wind power WT, photovoltaic PV, battery ES, gas turbine GT, ORC waste heat recovery device, heat storage tank TE and integrated energy station; Step 2: constructing a source-side robust optimization model of the regional multi-energy complementary energy system; Step 2.1, use formula (1) to obtain the mathematical model for handling uncertainty of wind and solar power; (1) In formula (1), Represents a set of uncertain parameters; Represents the uncertain elements in the mth row of the wind parameter matrix when the mathematical model constraints of wind power are abstracted into a matrix form. Represents the uncertain element in the mth row and nth column of the wind and solar parameter matrix; Indicates the uncertain element in row m and column n Randomly fluctuating parameter values; Indicates the uncertain element in row m and column n The fluctuation deviation value of Indicates the uncertain element in the mth row The number of represents the robust risk indicator of the mth row; Step 2.2: Use Equations (2) and (3) to obtain the robust equivalent model of wind and solar power. (2) (3) In formula (2), 、 are the actual output and maximum output of photovoltaic pv at time t respectively; 、 is the actual output and maximum output of wind turbine wt at time t; In formula (3), 、 and Both represent auxiliary variables introduced to deal with the uncertainty of photovoltaic power (PV) or wind power (WT); is the fluctuation amplitude of photovoltaic power pv or wind power wt at time t; Step 3: Use equations (4) and (5) to construct a universal optimization model of the load-side IGDT of the regional multi-energy complementary energy system: (4) In formula (4), is the coefficient of deviation of the uncertainty; is the maximum value of the objective function; is the benchmark value of the objective function; is the avoidance coefficient; 、 are equality constraints and inequality constraints respectively; is the objective function of the model; is the decision variable, is the decision variable The optimal value of is an uncertain quantity, Uncertain quantity The predicted value of is a set of uncertain quantities, and: (5) In formula (5), is the load forecast value of area q at time t; is the load value of area q at time t; is the set of uncertainties in region q at time t; Step 4, using formula (6) to construct a mathematical model of comprehensive carbon emissions of the regional multi-energy complementary energy system; (6) In formula (6), is the carbon emissions generated by the i-th integrated energy station at time t; is the comprehensive carbon emissions of the regional multi-energy complementary energy system; Step 5: Use formula (7) to construct a mathematical model of power balance constraints between the integrated energy stations in the regional multi-energy complementary energy system: (7) In formula (7), 、 are the electric energy purchased and sold by the i-th and j-th integrated energy stations from other integrated energy stations at time t, respectively; j∈[1,Y]; Step 6: Construct a mathematical model of power balance constraints for each integrated energy station in the regional multi-energy complementary energy system. Step 6.1: Use equation (8) to obtain the power balance constraint of the internal power grid of the integrated energy station at time t: (8) In formula (8), is the electric power output by the gas turbine gt at time t; is the purchased electricity at time t; is the interaction power with the battery es at time t; is the electric energy generated by the ORC waste heat recovery device at time t; 、 are the electric vehicle load and electricity load of the integrated energy station at time t respectively; Step 6.2: Use equation (9) to obtain the power balance constraint of the heating network of the integrated energy station at time t: (9) In formula (9), and are the thermal energy charging and discharging powers of the heat storage tank te at time t respectively; is the output thermal power of the gas turbine gt at time t; is the waste heat power input to the ORC waste heat power generation device at time t; Step 7: Comprehensive carbon emissions of regional multi-energy complementary energy systems The minimum is used as the objective function, and together with the mathematical model of the regional multi-energy complementary energy system, the power balance constraints between the integrated energy stations, and the power balance constraints of the internal power grid and heat network of each integrated energy station, the overall model of the regional multi-energy complementary energy system is formed; Step 8: Use the branch and bound method to solve the overall model of the regional multi-energy complementary energy system to obtain a robust optimization scheduling method.

2. The method for optimizing and dispatching a regional multi-energy complementary energy system based on source-load uncertainty according to claim 1 is characterized in that: The step 1 comprises: Step 1.1, establish the mathematical model of the storage battery es and the heat storage tank te; Step 1.1.1, use equations (10) and (11) to establish the mathematical model of the battery es: (10) In formula (10), 、 is the real-time power of the battery es at time t and time t-1; is the self-consumption coefficient of the storage battery es; 、 is the charge and discharge power of the battery es at time t; 、 is the charge and discharge efficiency of the battery es at time t; (11) In formula (11), 、 is the minimum and maximum capacity factor of the storage battery es; is the rated capacity of the storage battery es; 、 is the charge of the battery es at the initial and final moments; 、 is the charge and discharge state of the battery es at time t; is the power capacity ratio of the storage battery es; Step 1.1.2, use equations (12) and (13) to establish the mathematical model of the heat storage tank te: (12) In formula (12), and are the thermal energy capacity stored in the heat storage tank te at time t and time t-1 respectively; is the self-loss coefficient of the heat storage tank te; and are the heat energy charging and discharging efficiencies of the heat storage tank te respectively; and are the thermal energy charging and discharging powers of the heat storage tank te at time t respectively; is the time step; (13) In formula (13), is the state variable representing the charging and heating operation state of the heat storage tank te at time t; is the total capacity of the heat storage tank te; 、 are the capacities of the heat storage tank te at the initial and final moments respectively; Step 1.2, establish mathematical models of photovoltaic (PV), wind power (WT), gas turbine (GT), and ORC waste heat recovery device; Step 1.2.1, use formula (14) to establish the mathematical model of photovoltaic PV: (14) In formula (14), is the rated power of photovoltaic pv at time t; is the external irradiance at time t; is the standard irradiance; is the power temperature coefficient of photovoltaic pv; is the actual operating temperature of photovoltaic pv at time t; is the standard operating temperature of photovoltaic pv; Step 1.2.2: Use formula (15) to establish the mathematical model of wind power wt: (15) In formula (15), is the rated power of wind power wt; is the wind speed at time t; is the cut-in wind speed of wind power wt; is the wind speed at rated power; is the cut-out wind speed of wind power wt; Step 1.2.3: Use Equations (16) to (18) to obtain the mathematical model of the gas turbine gt: (16) (17) In formula (16), 、 are the natural gas power consumed and the electric power generated by the gas turbine gt at time t, respectively; is the power generation efficiency of the gas turbine gt; In formula (17), is the output thermal power of the gas turbine gt at time t; is the thermal-to-electricity ratio of the gas turbine gt; (18) In formula (18), Indicates the minimum load rate of the gas turbine gt; is the capacity of the gas turbine gt; In step 1.2.4, the mathematical model of the ORC waste heat recovery device is obtained using equations (19) and (20): (19) In formula (19), 、 are the power generation power and input waste heat power of the ORC waste heat power generation device at time t respectively; The power generation efficiency of the waste heat power generation device; (20) In formula (20), 、 are the minimum and maximum values ​​of the waste heat power input to the ORC waste heat power generation device at time t, respectively; and are the minimum and maximum values ​​of the ramp power of the ORC waste heat power generation device respectively; Step 1.3: Establish a mathematical model of carbon emissions from integrated energy stations using equations (21) to (23); (21) In formula (21), and are the standard coal conversion coefficients of electric energy e and natural gas gas respectively; is the carbon emission coefficient of complete combustion of standard coal; (22) In formula (22), 、 are the electricity purchased by any integrated energy station at time t and the losses incurred during the transmission of electricity by the integrated energy station; and represents the electricity sales volume and electric vehicle load of any integrated energy station at time t; is the transmission efficiency coefficient of any integrated energy station; (23) In formula (23), is the actual output of the gas turbine at time t.

3. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the regional multi-energy complementary energy system optimization scheduling method according to claim 1 or 2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing and scheduling a regional multi-energy complementary energy system according to claim 1 or 2 are executed.

Images