Economic benefit and low-carbon property dual-objective planning method and device for park multi-energy system

Abstract

The invention provides a park multi-energy system economic benefit and low carbon dual-objective planning method and device, and belongs to the field of comprehensive energy systems. The method comprises the steps of collecting renewable energy output and electric heating load scene data of a target park, and generating flexible load constraints of a power grid and a heat supply network; establishing a multi-stage dual-target collaborative planning architecture; converting the first-stage equipment scale planning model and the second-stage optimization scheduling model into a single-stage planning model based on a random variable and a typical scene; and determining a multi-energy system economic benefit and low-carbon dual-target planning strategy of the target park based on the flexible load constraint of the power grid and the heat supply network and the single-stage planning model. According to the method, the direct influence of different facility scales on the economic operation cost and the emission reduction potential of the comprehensive energy system can be deeply understood and quantified, so that the double targets of effectively coordinating the equipment capacity and the overall economic-environmental benefit of the system in the initial planning stage can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to a method and device for dual-objective planning of economic benefits and low carbon performance for a multi-energy system in a park. Background Art

[0002] Faced with the urgent need for a low-carbon transition, the power system is undergoing a fundamental transformation. Integrated energy systems, as an innovative model, are becoming a key approach to improving energy efficiency by leveraging their integrated and complementary advantages across multiple energy sources. This system abandons the traditional model of operating a single energy source independently. By integrating the supply and demand management of multiple energy sources, including electricity, heat, and gas, it strives to achieve efficient and low-carbon energy utilization. This has far-reaching implications for promoting energy efficiency upgrades, optimizing the energy mix, reducing environmental pollution, and ensuring sustainable development.

[0003] However, achieving a win-win situation of economic benefits and low-carbon goals is not easy, especially in the planning and scheduling practices of integrated energy systems. With the increasing instability of renewable energy generation and the unpredictability of electricity and heat demand, the complexity of the problem has further increased. How to accurately model demand flexibility, rationally plan equipment capacity, and scientifically balance economic efficiency and environmental impact while ensuring efficient system operation has become a core challenge in current engineering practice. In particular, designing models and algorithms that can adapt to changes in energy supply and demand while accurately reflecting the economic value and environmental contribution of equipment is particularly critical for promoting the coordinated economic and low-carbon development of integrated energy systems. Summary of the Invention

[0004] The embodiment of the present invention provides a dual-objective planning method and device for economic benefit and low-carbon performance of a multi-energy system in a park, so as to solve the problem that the economic-low-carbon coordinated development of the integrated energy system cannot be accurately and reasonably promoted at this stage.

[0005] In a first aspect, an embodiment of the present invention provides a dual-objective planning method for a multi-energy system in a park, which includes:

[0006] Collect renewable energy output and electric and thermal load scenario data for the target park, and generate flexible load constraints for the power grid and heat network;

[0007] A dual-objective collaborative planning framework is established based on scenario data. This framework includes a first-stage equipment sizing model based on the economic benefits and annual return on investment of each energy source, and a second-stage optimization scheduling model based on the economic benefits and carbon emissions of each energy source.

[0008] Based on random variables and typical scenarios, the first-stage equipment size planning model and the second-stage optimization scheduling model are transformed into a single-stage planning model;

[0009] Based on the flexible load constraints of the power grid and heat network and a single-stage planning model, a dual-objective planning strategy of economic benefits and low carbon for the multi-energy system of the target park is determined.

[0010] In one possible implementation, the flexible load constraints of the power grid and the heat network are:

[0011]

[0012] in, are the upper and lower limits of fluctuations in electricity and heat demand at time t; is the electricity and heat demand at time t; e and λ h is the demand fluctuation coefficient;

[0013] Among them, the flexibility requirements of electrical load and thermal load at time t are:

[0014]

[0015] In one possible implementation, the first-stage equipment scale planning model includes a first objective function;

[0016] The first objective function is:

[0017]

[0018] Among them, C inv represents the total annual investment economic benefits of all equipment; r represents the capital interest rate; y represents the annual investment return; and Represent the economic benefits of unit production capacity investment of gas turbine, gas boiler, battery and heat storage tank respectively, and Represent the energy production of gas turbine, gas boiler, battery and heat storage tank respectively.

[0019] In one possible implementation, the first stage equipment scale planning model includes a first constraint condition;

[0020] The first constraint is:

[0021]

[0022] In one possible implementation, the second-stage optimization scheduling model includes a second objective function;

[0023] The second objective function is:

[0024] min{f1,f2}

[0025] st grid constraints

[0026] Heating network constraints

[0027] f1=C grid +C main +C fuel

[0028] f2=CO 2e +CO 2h +CO 2grid

[0029] Among them, f1 is the economic benefit objective function of the integrated energy system operation; f2 is the carbon emission objective function of the integrated energy system; C grid For the economic benefit of electricity purchase, C main To maintain economic benefits for equipment, C fuel For fuel economy benefits, CO 2e The carbon emissions generated by the power system, CO 2h Carbon emissions from heating systems, CO 2grid Carbon emissions generated from purchasing and selling electricity to the upper-level power grid.

[0030] In one possible implementation, the second-stage optimization scheduling model includes a second constraint condition;

[0031] The second constraint condition includes the economic benefit constraint condition of electricity purchase, the economic benefit constraint condition of equipment maintenance, the economic benefit constraint condition of equipment operation, the carbon emission constraint condition of the power system, the carbon emission constraint condition of the heating system, and the carbon emission constraint condition of the upper-level power grid purchasing and selling electricity;

[0032] Among them, the economic benefit constraints of electricity purchase are:

[0033]

[0034] in, P is the electricity purchase and sales price of the integrated energy system at time t; t buy 、P t sell The electricity purchased and sold by the integrated energy system at time t;

[0035] The economic benefit constraints of equipment maintenance are:

[0036]

[0037] Among them, m i is the economic benefit of maintenance of the i-th unit; m j is the economic benefit of maintaining the jth energy storage device; P t,i is the output of the i-th unit at time t; and are the energy charged and discharged by the j-th energy storage device at time t; Ω G and Ω C They are respectively a collection of gas units and energy storage equipment;

[0038] The constraints on the economic benefits of equipment operation are:

[0039]

[0040] Among them, c gas is the unit natural gas economic benefit coefficient; are the electrical output and thermal output of the gas turbine and gas boiler at time t, respectively; η gt ,η gb are the efficiencies of gas turbine and gas boiler respectively;

[0041] The constraints on carbon emissions from the power system are:

[0042]

[0043] Among them, γ gt is the CO2 emission coefficient of gas turbine power generation; wind turbines are clean energy power generation, and their CO2 emissions are 0;

[0044] The carbon emission constraints of the heating system are:

[0045]

[0046] Among them, γ gb is the carbon dioxide emission coefficient of heat production by gas boiler;

[0047] The carbon emission constraints generated by the purchase and sale of electricity by the upper-level power grid are:

[0048]

[0049] Among them, γ grid It is the equivalent carbon dioxide emission coefficient of purchasing and selling electricity to the superior power grid.

[0050] In one possible implementation, based on random variables and typical scenarios, the first-stage equipment sizing model and the second-stage optimization scheduling model are transformed into a single-stage planning model, including:

[0051] A stratified sampling method was used, with renewable energy output and electric and thermal load scenario data as the random variable population. The random variable population was divided into multiple levels, and samples were randomly selected from each level as random variables. Each level included at least one typical scenario.

[0052] Among them, the single-stage planning model is:

[0053]

[0054] stAx≤b

[0055] Ex+Fy i,j -Gu i,j ≤hi=1,2,...,L,j=1,2,...,n i

[0056] Where u i,j is the jth sample extracted from layer i; p i is the probability of the i-th layer, p i =n i / N; x is the first stage decision variable; y i,j is the second-stage decision variable, corresponding to each sampling scenario u i,j ; b, h are constant vectors; A, E, F, G are constant matrices.

[0057] In one possible implementation, based on flexible load constraints of the power and heat networks and a single-stage planning model, a dual-objective planning strategy for the multi-energy system in the target park, focusing on both economic efficiency and low carbon performance, is determined. This strategy includes:

[0058] Based on the flexible load constraints of the power grid and heat network, the economic benefit objective function and the carbon emission objective function of the integrated energy system in the single-stage planning model are solved, and the optimal solutions corresponding to the economic benefit objective function and the carbon emission objective function of the integrated energy system are obtained.

[0059] Based on the optimal solution corresponding to the integrated energy system operation economic benefit objective function and the optimal solution corresponding to the integrated energy system carbon emission objective function, the integrated energy system operation economic benefit objective function and the integrated energy system carbon emission objective function are standardized to obtain standardized target values;

[0060] Based on the standardized target values, the dual-objective planning strategy of economic benefits and low carbon of the multi-energy system of the target park is determined.

[0061] In one possible implementation, based on the standardized target values, a dual-objective planning strategy for the multi-energy system economic benefits and low-carbon performance of the target park is determined, including:

[0062] Based on the standardized target value, multiple Pareto points are obtained;

[0063] Perform positive processing on the target value on the Pareto frontier and convert the minimization index into the maximization index;

[0064] Normalize the positive target value and calculate the distance between the ideal point and the negative ideal point corresponding to the normalized target value;

[0065] Determine the score of each Pareto point based on the distance between the ideal point and the negative ideal point corresponding to the standardized target value;

[0066] The target value corresponding to the Pareto point with the highest score is used as the dual-objective planning strategy of economic benefits and low carbon of the multi-energy system of the target park.

[0067] In a second aspect, an embodiment of the present invention provides a dual-objective planning device for a multi-energy system in a park, which includes:

[0068] The acquisition module is used to collect renewable energy output and electric and thermal load scenario data of the target park and generate flexible load constraints for the power grid and heat network;

[0069] A construction module for establishing a dual-objective collaborative planning framework based on scenario data. The dual-objective collaborative planning framework includes a first-stage equipment sizing model based on the economic benefits and annual return on investment of each energy source, and a second-stage optimization scheduling model based on the economic benefits and carbon emissions of each energy source.

[0070] A conversion module, which is used to convert the first-stage equipment size planning model and the second-stage optimization scheduling model into a single-stage planning model based on random variables and typical scenarios;

[0071] The planning module is used to determine the dual-objective planning strategy of economic benefits and low carbon for the multi-energy system of the target park based on the flexible load constraints of the power grid and heat network and the single-stage planning model.

[0072] The embodiment of the present invention provides a dual-objective planning method and device for economic benefits and low carbon of a multi-energy system in a park. Compared with traditional methods, the embodiment of the present invention comprehensively considers the uncertainty factors inherent in the system, covering the instability of renewable energy output and the volatility of energy demand, and uses the constructed dual-objective optimization model to simultaneously pursue the goals of maximizing financial benefits and minimizing greenhouse gas emissions. By fine-tuning the collaborative operation of various energy resources and energy storage facilities, the scheduling strategy of the integrated energy system is innovated. On this basis, not only the economic feasibility of the system is ensured, but also the effective management and significant reduction of carbon emissions are achieved, promoting the harmonious unity of economic benefits and ecological protection. Compared with the existing technology, the embodiment of the present invention is more accurate in dealing with uncertainties inside and outside the system, significantly enhancing the flexibility and resilience of the energy system, and contributing practical tools and strategies to promote the transformation of the integrated energy system to a low-carbon model.

[0073] The embodiments of the present invention will greatly improve the decision-making quality of integrated energy project planning and operation and maintenance, scientifically guide the efficient conversion and utilization of energy, accelerate the development of a low-carbon economy, and have milestone significance for promoting a sustainable future for the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0075] Figure 1 A structural diagram of an integrated energy system provided by an embodiment of the present invention;

[0076] Figure 2 This is a flowchart for implementing the dual-objective planning method for economic benefits and low carbon performance of a multi-energy system in a park provided by an embodiment of the present invention;

[0077] Figure 3(a)-Figure 3(d) It is a photovoltaic output and multi-energy flexible demand dynamic scenario database provided by an embodiment of the present invention;

[0078] Figure 4(a)-Figure 4(d) This is a diagram of the composition of electric heating power in two representative scenarios provided by an embodiment of the present invention;

[0079] Figure 5 This is a flowchart of a dual-objective planning method for economic benefits and low carbon performance of a multi-energy system in a park provided by another embodiment of the present invention;

[0080] Figure 6 It is a structural diagram of a dual-objective planning device for economic benefits and low carbon performance of a multi-energy system in a park provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0082] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0083] The embodiment of the present invention operates in an integrated energy system, wherein the integrated energy system is as follows: Figure 1As shown in Figure 2, this integrated energy system includes three energy networks: electricity, heat, and gas, meeting both energy needs. The energy supply units include photovoltaics, gas turbines, and gas boilers. The energy storage system consists of heat storage tanks and batteries.

[0084] Figure 2 This is a flow chart of the implementation of the dual-objective planning method for the economic benefit and low-carbon nature of the park multi-energy system provided by the embodiment of the present invention. Figure 2 As shown, the method includes:

[0085] Step 110: Collect renewable energy output and electric and thermal load scenario data of the target park, and generate flexible load constraints for the power grid and thermal network.

[0086] In this embodiment, the flexible load constraints of the power grid and the heat network can be expressed as:

[0087]

[0088] in, are the upper and lower limits of fluctuations in electricity and heat demand at time t; is the electricity and heat demand at time t; e and λ h is the demand fluctuation coefficient;

[0089] Among them, the flexibility requirements of electrical load and thermal load at time t are:

[0090]

[0091] Step 120: Establish a dual-objective collaborative planning framework based on the scenario data, wherein the dual-objective collaborative planning framework includes a first-stage equipment scale planning model determined based on the economic benefits and annual investment returns of each energy source, and a second-stage optimization scheduling model determined based on the economic benefits and carbon emissions of each energy source.

[0092] In this embodiment, the first stage equipment scale planning model may include a first objective function;

[0093] Among them, the first objective function can be expressed as:

[0094]

[0095] Among them, C inv represents the total annual investment economic benefits of all equipment; r represents the capital interest rate; y represents the annual investment return; and Represent the economic benefits of unit production capacity investment of gas turbine, gas boiler, battery and heat storage tank respectively, and Represent the energy production of gas turbine, gas boiler, battery and heat storage tank respectively.

[0096] The first stage equipment size planning model may also include a first constraint;

[0097] The first constraint is:

[0098]

[0099] The second stage optimization scheduling model includes a second objective function;

[0100] Among them, the second objective function is:

[0101] min{f1,f2}

[0102] st grid constraints

[0103] Heating network constraints

[0104] f1=C grid +C main +C fuel

[0105] f2=CO 2e +CO 2h +CO 2grid

[0106] Among them, f1 is the economic benefit objective function of the integrated energy system operation; f2 is the carbon emission objective function of the integrated energy system; C grid For the economic benefit of electricity purchase, C main To maintain economic benefits for equipment, C fuel For fuel economy benefits, CO 2e The carbon emissions generated by the power system, CO 2h Carbon emissions from heating systems, CO 2grid Carbon emissions generated from purchasing and selling electricity to the upper-level power grid.

[0107] The second stage optimization scheduling model may include a second constraint;

[0108] Exemplarily, the second constraint may include constraints on the economic benefits of electricity purchase, constraints on the economic benefits of equipment maintenance, constraints on the economic benefits of equipment operation, constraints on carbon emissions generated by the power system, constraints on carbon emissions generated by the heating system, and constraints on carbon emissions generated by electricity purchase and sales by the upper-level power grid.

[0109] Among them, the economic benefit constraints of electricity purchase are:

[0110]

[0111] in, P is the electricity purchase and sales price of the integrated energy system at time t;t buy 、P t sell The electricity purchased and sold by the integrated energy system at time t;

[0112] The economic benefit constraints of equipment maintenance are:

[0113]

[0114] Among them, m i is the economic benefit of maintenance of the i-th unit; m j is the economic benefit of maintaining the jth energy storage device; P t,i is the output of the i-th unit at time t; and are the energy charged and discharged by the j-th energy storage device at time t; Ω G and Ω C They are respectively a collection of gas units and energy storage equipment;

[0115] The constraints on the economic benefits of equipment operation are:

[0116]

[0117] Among them, c gas is the unit natural gas economic benefit coefficient; are the electrical output and thermal output of the gas turbine and gas boiler at time t, respectively; η gt ,η gb are the efficiencies of gas turbine and gas boiler respectively;

[0118] The constraints on carbon emissions from the power system are:

[0119]

[0120] Among them, γ gt is the CO2 emission coefficient of gas turbine power generation; wind turbines are clean energy power generation, and their CO2 emissions are 0;

[0121] The carbon emission constraints of the heating system are:

[0122]

[0123] Among them, γ gb is the carbon dioxide emission coefficient of heat production by gas boiler;

[0124] The carbon emission constraints generated by the purchase and sale of electricity by the upper-level power grid are:

[0125]

[0126] Among them, γ gridIt is the equivalent carbon dioxide emission coefficient of purchasing and selling electricity to the superior power grid.

[0127] In addition to the above constraints, the constraints may also include grid tie line capacity constraints:

[0128]

[0129] in, represents the active power flow on branch b at time t; Indicates the upper limit of active power flow in branch b; V i Indicates the upper and lower limits of the voltage at node i.

[0130] The pipeline constraints of the heating network include heat source power balance constraints, power and temperature equations of heat source and heat demand nodes, pipeline transmission delay and heat loss constraints, heating network node power balance constraints, heating network node water temperature mixing constraints, heating network supply and return water temperature upper and lower limit constraints and building heat demand constraints.

[0131] Correspondingly, the heat source power balance constraint can be:

[0132]

[0133] in, E is the thermal power variable injected into the heating network during period t; gb 、E tst An index collection for gas boilers and heat storage devices; is the heat output power variable of gas boiler i during period t; are the heat storage and heat release of the i-th heat storage device at time t respectively.

[0134] The power and temperature equations for the heat source and heat demand nodes can be:

[0135]

[0136] in, are the pipeline index sets flowing out of / into node k respectively; Φ sn , Φ ln are the index sets of source nodes and demand nodes in the heating network respectively; c w is the specific heat capacity of water; m j is the mass flow rate of heat medium in pipeline j; is the heat medium temperature variable at node k in the water supply and return network at time t; is the thermal demand power variable at node k during period t;

[0137] The pipeline transmission delay and heat loss constraints are:

[0138]

[0139] Among them, Φ p The index collection of heat network pipelines; is the coefficient related to pipeline j and transmission delay; parameter β j is the insulation coefficient of pipe j; is the pipeline ambient temperature at time t; is the temperature variable of the heat medium at the inlet and outlet of the water supply pipe j at time t; is the heat medium temperature variable at the inlet and outlet of the return pipe j at time t.

[0140] The power balance constraint of the heating network node can be:

[0141]

[0142] Among them, Φ in is the set of intersection nodes in the heat network; is the heat medium temperature at node k in the water supply and return network during period t.

[0143] The water temperature mixing constraint of the heating network node can be:

[0144]

[0145] Among them, Φ in is the set of intersection nodes in the heat network; is the heat medium temperature at node k in the water supply and return network during period t.

[0146] The upper and lower limits of the supply and return water temperatures in the heating network are:

[0147]

[0148] in, τ s The upper and lower limits of water supply temperature for the heating network; τ r The upper and lower limits of the heat network return water temperature.

[0149] The energy storage / discharge constraints of the energy storage device can be:

[0150] Wherein, j represents the battery bt or the heat storage tank tst; A variable 0-1 representing the charge and discharge flags for the j-th energy storage device at time t; Charge or discharge energy for the j-th energy storage device at time t; E is the maximum energy storage value of the j-th energy storage device at time t; t,j ,E t-1,j is the total energy stored in the j-th energy storage device at time t and time t-1; is the maximum or minimum coefficient of energy storage of the jth type of energy storage device; is the upper limit coefficient of charge and discharge; η ch,j / η dis,j The charging or discharging efficiency of the j-th energy storage device.

[0151] The building heat demand constraint can be:

[0152]

[0153] in, is the building parameter at the heat network demand node k; is the indoor temperature variable of the building at the demand node k of the heat network during period t; is the building heat demand heating power variable at the heat network demand node k during period t; is the outdoor temperature of the building; N is the length of the scheduling cycle; τ in The upper and lower limits of the building room temperature.

[0154] Step 130: Based on random variables and typical scenarios, the first-stage equipment scale planning model and the second-stage optimization scheduling model are converted into a single-stage planning model.

[0155] In this embodiment, a stratified sampling method can be used to take the renewable energy output and electric and thermal load scenario data as the random variable population, and the random variable population is divided into multiple levels, and samples are randomly selected in each level as random variables; wherein each level includes at least one typical scenario.

[0156] The type sampling method divides the random variables of the population into multiple types and randomly extracts samples from each type, making the extracted samples more representative and thus more accurately reflecting the uncertainty in multiple scenarios:

[0157] Divide the random variable u into L different types u1,u2,...,u L , the number of samples drawn from each class is n i The total sample size is

[0158] From u i The randomly selected samples in the class are:

[0159] The total representative scene samples of all classes are:

[0160] Accordingly, based on the type sampling scenario, the optimization problem, that is, the single-stage planning model can be constructed as follows:

[0161]

[0162] stAx≤b

[0163] Ex+Fy i,j -Gu i,j ≤hi=1,2,...,L,j=1,2,...,n i

[0164] Where u i,j is the jth sample extracted from layer i; p i is the probability of the i-th layer, p i =n i / N; x is the first stage decision variable; y i,j is the second-stage decision variable, corresponding to each sampling scenario u i,j ; b, h are constant vectors; A, E, F, G are constant matrices.

[0165] Step 140: Based on the flexible load constraints of the power grid and the heat network and the single-stage planning model, determine the dual-objective planning strategy of economic benefits and low carbon performance of the multi-energy system of the target park.

[0166] In this embodiment, the nearest neighbor classifier (NNC) can be used to solve the single-stage planning model to obtain the Pareto frontier between economic benefits and carbon emissions. The integrated energy system operation economic benefit objective function f1 and the integrated energy system carbon emission objective function f2 in the single-stage planning model are solved to obtain the optimal solution corresponding to f1 and the optimal solution corresponding to f2;

[0167] The optimization targets, i.e. f1 and f2, are standardized and the target values ​​after standardization can be recorded as and

[0168] Available,

[0169] in, and They are the objective functions f j The minimum and maximum values ​​of .

[0170] Given the direction of the Utopia line And given the number of solutions K, generate evenly distributed points on the Utopia line

[0171] Solve the following optimization problem for the evenly distributed points on the Utopia line to generate Pareto points; take the jth point as an example:

[0172]

[0173] in,

[0174] According to the calculation results of each Pareto point, the target value of the original problem is calculated through inverse transformation.

[0175] Accordingly, the low-carbon optimization scheduling scheme of the integrated energy system can be obtained through the following steps:

[0176] Positive data indicator: The target value on the Pareto front is positively processed to convert the minimized indicator into the maximized indicator.

[0177] or

[0178] Data standardization: The data after forward transformation is standardized to eliminate the dimensional impact between different targets.

[0179] Calculate the distance between the ideal point and the negative ideal point: For each point n, n = 1, 2, ..., N on the Pareto frontier, calculate its Euclidean distance to the Utopia point and the negative ideal point respectively. and

[0180] in, is the coordinate of the nth point on the Pareto frontier; p utp is the coordinate of the Utopia point; p nip is the negative ideal point coordinate;

[0181] Calculate the score: For point n on the Pareto front, n=1,2,…,N, the score is:

[0182] The larger the score, the closer the point is to the ideal solution.

[0183] The scores of points n, n = 1, 2, ..., N, on the Pareto front are sorted, and the point with the highest score and its corresponding decision variable value are selected as the final low-carbon optimization scheduling plan for the integrated energy system.

[0184] To verify the method provided in this embodiment, an example is given below to illustrate:

[0185] The multi-energy flow system of the embodiment of the present invention includes three energy sources: electricity and heat, which can meet the needs of electricity and heat. Its structure is as follows: Figure 1As shown, the system includes one 10MW gas turbine, one 10MW gas boiler, and four 1MW photovoltaic units. The optimization period is 24 hours, the scheduling interval is 1 hour, and the weighting method is used to set the weights for the economic benefit objective function and the carbon emission objective function to 0.5 and 0.5, respectively. This example includes four representative scenarios: spring, summer, autumn, and winter, and the scenario probabilities are equal, all 0.25.

[0186] According to the steps provided in the embodiment of the present invention, the economic and low-carbon dual-objective stochastic programming of the integrated energy system is carried out, and the database of four typical daily renewable energy output and electric and heat demand dynamic scenarios is as follows: Figure 3(a)-Figure 3(d) As shown. Among them, Figure 3(a)-Figure 3(d) Each corresponds to a typical scenario. In each scenario, the horizontal axis is the corresponding time and the vertical axis is the power. Each figure includes two data: photovoltaic and electric load.

[0187] Taking the scenarios corresponding to Figure 3(b) and Figure 3(d) as an example, assuming that the economic benefit planning result of the equipment investment in the first phase is 263.5401×10 4 ￥, of which the gas turbine capacity is 4582kW, the gas boiler capacity is 2224kW, and the battery and heat storage tank capacity is 0. The economic benefit of the second phase system operation is 2683.4×10 4 ￥, the total carbon emissions are 13235t. Correspondingly, Figure 4(a)-Figure 4(d) The electric and thermal power components for these two typical scenarios are given. Figure 4(a) shows the electric balance diagram for the scenario in Figure 3(b), and Figure 4(b) shows the thermal balance diagram for the scenario in Figure 3(b); Figure 4(c) shows the electric balance diagram for the scenario in Figure 3(d), and Figure 4(d) shows the thermal balance diagram for the scenario in Figure 3(d).

[0188] It can be seen that in different scenarios, there are significant differences in the equipment scale, economic benefits and carbon emissions. And the output composition of the equipment is also different in different scenarios. The method provided by the embodiment of the present invention is good at dealing with various typical daily changes, and comprehensively taking into account many variables within the integrated energy system, such as the intermittent power generation of renewable energy and the instability of energy consumption. By adopting a dual-objective optimization model, it cleverly balances the needs of economic returns and carbon emission reduction, and proposes a set of compromise strategies, which successfully alleviates the contradiction between the pursuit of maximizing economic benefits and minimizing carbon emissions, finds the best energy layout path, and promotes high efficiency of energy use and significant carbon emission reduction effects.

[0189] Figure 5 This is a flowchart of a dual-objective planning method for economic benefits and low carbon performance of a multi-energy system in a park provided by another embodiment of the present invention. Figure 5 As shown, the method includes:

[0190] Generate a dynamic scenario database of wind power output and multi-energy flexible demand; establish a multi-stage dual-objective collaborative planning framework based on the database (i.e., the first-stage equipment scale planning model and the second-stage optimization scheduling model); use type sampling technology to select typical scenarios from the database and establish a deterministic planning problem; use the NNC method to solve and obtain multiple solution results, and use the TOPSIS comprehensive evaluation method to select the optimal strategy from the multiple solution results as the dual-objective planning strategy for the multi-energy system economic benefits and low-carbon nature of the target park.

[0191] In summary, the economic-low-carbon collaborative stochastic programming method proposed in the embodiment of the present invention provides a powerful tool for the planning, design, and operation management of integrated energy systems. It not only improves energy utilization efficiency and reduces carbon emissions, but also ensures economic competitiveness and sustainable development capabilities in the face of future energy market uncertainties.

[0192] It should be understood that the order of execution of the steps in the above embodiments does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0193] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0194] Figure 6 The following is a schematic diagram showing the structure of a dual-objective planning device for economic benefits and low carbon performance of a multi-energy system in a park provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows:

[0195] like Figure 6 As shown, the dual-objective planning device 6 for the economic benefit and low-carbon performance of the park multi-energy system includes:

[0196] The acquisition module 61 is used to collect renewable energy output and electric and thermal load scenario data of the target park and generate flexible load constraints for the power grid and heat network;

[0197] A construction module 62 is configured to establish a dual-objective collaborative planning framework based on the scenario data, wherein the dual-objective collaborative planning framework includes a first-stage equipment sizing model determined based on the economic benefits and annual return on investment of each energy source, and a second-stage optimization scheduling model determined based on the economic benefits and carbon emissions of each energy source;

[0198] A conversion module 63 is used to convert the first-stage equipment scale planning model and the second-stage optimization scheduling model into a single-stage planning model based on random variables and typical scenarios;

[0199] The planning module 64 is used to determine the dual-objective planning strategy of economic benefits and low carbon performance of the multi-energy system of the target park based on the flexible load constraints of the power grid and the heat network and the single-stage planning model.

[0200] In one possible implementation, the flexible load constraints of the power grid and the heat network are:

[0201]

[0202] in, are the upper and lower limits of fluctuations in electricity and heat demand at time t; is the electricity and heat demand at time t; e and λ h is the demand fluctuation coefficient;

[0203] Among them, the flexibility requirements of electrical load and thermal load at time t are:

[0204]

[0205] In one possible implementation, the first-stage equipment scale planning model includes a first objective function;

[0206] The first objective function is:

[0207]

[0208] Among them, C inv represents the total annual investment economic benefits of all equipment; r represents the capital interest rate; y represents the annual investment return; and Represent the economic benefits of unit production capacity investment of gas turbine, gas boiler, battery and heat storage tank respectively, and Represent the energy production of gas turbine, gas boiler, battery and heat storage tank respectively.

[0209] In one possible implementation, the first stage equipment scale planning model includes a first constraint condition;

[0210] The first constraint is:

[0211]

[0212] In one possible implementation, the second-stage optimization scheduling model includes a second objective function;

[0213] The second objective function is:

[0214] min{f1,f2}

[0215] st grid constraints

[0216] Heating network constraints

[0217] f1=C grid +C main +C fuel

[0218] f2=CO 2e +CO 2h +CO 2grid

[0219] Among them, f1 is the economic benefit objective function of the integrated energy system operation; f2 is the carbon emission objective function of the integrated energy system; C grid For the economic benefit of electricity purchase, C main To maintain economic benefits for equipment, C fuel For fuel economy benefits, CO 2e The carbon emissions generated by the power system, CO 2h Carbon emissions from heating systems, CO 2grid Carbon emissions generated from purchasing and selling electricity to the upper-level power grid.

[0220] In one possible implementation, the second-stage optimization scheduling model includes a second constraint condition;

[0221] The second constraint condition includes the economic benefit constraint condition of electricity purchase, the economic benefit constraint condition of equipment maintenance, the economic benefit constraint condition of equipment operation, the carbon emission constraint condition of the power system, the carbon emission constraint condition of the heating system, and the carbon emission constraint condition of the upper-level power grid purchasing and selling electricity;

[0222] Among them, the economic benefit constraints of electricity purchase are:

[0223]

[0224] in, P is the electricity purchase and sales price of the integrated energy system at time t; t buy 、P t sell The electricity purchased and sold by the integrated energy system at time t;

[0225] The economic benefit constraints of equipment maintenance are:

[0226]

[0227] Among them, m i is the economic benefit of maintenance of the i-th unit; m j is the economic benefit of maintaining the jth energy storage device; P t,i is the output of the i-th unit at time t; and are the energy charged and discharged by the j-th energy storage device at time t; Ω G and Ω C They are respectively a collection of gas units and energy storage equipment;

[0228] The constraints on the economic benefits of equipment operation are:

[0229]

[0230] Among them, c gas is the unit natural gas economic benefit coefficient; are the electrical output and thermal output of the gas turbine and gas boiler at time t, respectively; η gt ,η gb are the efficiencies of gas turbine and gas boiler respectively;

[0231] The constraints on carbon emissions from the power system are:

[0232]

[0233] Among them, γ gt is the CO2 emission coefficient of gas turbine power generation; wind turbines are clean energy power generation, and their CO2 emissions are 0;

[0234] The carbon emission constraints of the heating system are:

[0235]

[0236] Among them, γ gb is the carbon dioxide emission coefficient of heat production by gas boiler;

[0237] The carbon emission constraints generated by the purchase and sale of electricity by the upper-level power grid are:

[0238]

[0239] Among them, γ grid It is the equivalent carbon dioxide emission coefficient of purchasing and selling electricity to the superior power grid.

[0240] In a possible implementation, the conversion module 63 is specifically configured to:

[0241] A stratified sampling method was used, with renewable energy output and electric and thermal load scenario data as the random variable population. The random variable population was divided into multiple levels, and samples were randomly selected from each level as random variables. Each level included at least one typical scenario.

[0242] Among them, the single-stage planning model is:

[0243]

[0244] stAx≤b

[0245] Ex+Fy i,j -Gu i,j ≤hi=1,2,...,L,j=1,2,...,n i

[0246] Where u i,j is the jth sample extracted from layer i; p i is the probability of the i-th layer, p i =n i / N; x is the first stage decision variable; y i,j is the second-stage decision variable, corresponding to each sampling scenario u i,j ; b, h are constant vectors; A, E, F, G are constant matrices.

[0247] In one possible implementation, the planning module 64 is specifically configured to:

[0248] Based on the flexible load constraints of the power grid and heat network, the economic benefit objective function and the carbon emission objective function of the integrated energy system in the single-stage planning model are solved, and the optimal solutions corresponding to the economic benefit objective function and the carbon emission objective function of the integrated energy system are obtained.

[0249] Based on the optimal solution corresponding to the integrated energy system operation economic benefit objective function and the optimal solution corresponding to the integrated energy system carbon emission objective function, the integrated energy system operation economic benefit objective function and the integrated energy system carbon emission objective function are standardized to obtain standardized target values;

[0250] Based on the standardized target values, the dual-objective planning strategy of economic benefits and low carbon of the multi-energy system of the target park is determined.

[0251] In one possible implementation, the planning module 64 is specifically configured to:

[0252] Based on the standardized target value, multiple Pareto points are obtained;

[0253] Perform positive processing on the target value on the Pareto frontier and convert the minimization index into the maximization index;

[0254] Normalize the positive target value and calculate the distance between the ideal point and the negative ideal point corresponding to the normalized target value;

[0255] Determine the score of each Pareto point based on the distance between the ideal point and the negative ideal point corresponding to the standardized target value;

[0256] The target value corresponding to the Pareto point with the highest score is used as the dual-objective planning strategy of economic benefits and low carbon of the multi-energy system of the target park.

[0257] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0258] Those skilled in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0259] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the dual-objective planning method for economic benefits and low carbon of each park multi-energy system. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0260] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A dual-objective planning method for economic benefits and low carbon performance of a multi-energy system in a park, characterized in that: include: Collect renewable energy output and electric and thermal load scenario data of the target park, and generate flexible load constraints for the power grid and heat network; Establishing a dual-objective collaborative planning framework based on the scenario data, wherein the dual-objective collaborative planning framework includes a first-stage equipment scale planning model determined based on the economic benefits and annual investment returns of each energy source, and a second-stage optimization scheduling model determined based on the economic benefits and carbon emissions of each energy source; Based on random variables and typical scenarios, the first-stage equipment scale planning model and the second-stage optimization scheduling model are transformed into a single-stage planning model; Based on the flexible load constraints of the power grid and the heat network and the single-stage planning model, a dual-objective planning strategy of economic benefit and low carbon for the multi-energy system of the target park is determined.

2. The dual-objective planning method of economic benefit and low carbon performance of a multi-energy system in a park according to claim 1 is characterized in that: The flexible load constraints of the power grid and heat network are: in, are the upper and lower limits of the fluctuation of electricity demand and heat demand at time t; is the electricity and heat demand at time t; e and λ h is the demand fluctuation coefficient; Among them, the upward and downward flexibility requirements of electrical load and thermal load at time t are:

3. The dual-objective planning method of economic benefit and low carbon performance of a multi-energy system in a park according to claim 1 is characterized in that: The first stage equipment scale planning model includes a first objective function; The first objective function is: Among them, C inv represents the total annual investment economic benefits of all equipment; r represents the capital interest rate; y represents the annual investment return; and Respectively represent the economic benefits of unit capacity investment of gas turbine, gas boiler, battery and heat storage tank, and They represent the energy production of gas turbine, gas boiler, battery and heat storage tank respectively.

4. The dual-objective planning method of economic benefit and low carbon performance of a multi-energy system in a park according to claim 1 is characterized in that: The first stage equipment scale planning model includes a first constraint condition; The first constraint condition is:

5. The dual-objective planning method of economic benefit and low carbon performance of a multi-energy system in a park according to claim 1 is characterized in that: The second stage optimization scheduling model includes a second objective function; The second objective function is: min{f1,f2} Grid constraints Heating network constraints f1=C grid +C main +C fuel f2=WHAT 2e +WHAT 2h +WHAT 2grid Among them, f1 is the economic benefit objective function of the integrated energy system operation; f2 is the carbon emission objective function of the integrated energy system; C grid For the economic benefits of purchasing electricity, C main To maintain economic benefits for equipment, C fuel For fuel economy, CO 2e The carbon emissions generated by the power system, CO 2h Carbon emissions from heating systems, CO 2grid Carbon emissions generated by purchasing and selling electricity to the upper-level power grid.

6. The dual-objective planning method of economic benefit and low carbon performance of a multi-energy system in a park according to claim 1 is characterized in that: The second stage optimization scheduling model includes a second constraint condition; The second constraint conditions include the economic benefit constraint conditions of electricity purchase, the economic benefit constraint conditions of equipment maintenance, the economic benefit constraint conditions of equipment operation, the carbon emission constraint conditions of the power system, the carbon emission constraint conditions of the heating system and the carbon emission constraint conditions of the upper power grid purchasing and selling electricity; Among them, the economic benefit constraints of electricity purchase are: in, P is the electricity purchase and sales price of the comprehensive energy system at time t; t buy , P t sell is the electricity purchase and sales of the integrated energy system at time t; The equipment maintenance economic benefit constraint conditions are: Among them, m i is the economic benefit of maintenance of the i-th unit; m j is the economic benefit of maintaining the jth energy storage device; P t,i is the output of the i-th unit at time t; and are the energy charged and discharged by the j-th energy storage device at time t; Ω G and Ω C They are respectively a collection of gas units and energy storage equipment; The equipment operation economic benefit constraints are: Among them, c gas is the unit natural gas economic benefit coefficient; are the electrical output and thermal output of the gas turbine and gas boiler at time t respectively; η gt , η gb are the efficiencies of gas turbine and gas boiler respectively; The carbon emission constraints generated by the power system are: Among them, γ gt is the CO2 emission coefficient of gas turbine power generation; wind turbines are clean energy power generation, and their CO2 emissions are 0; The carbon emission constraints generated by the heating system are: Among them, γ gb is the carbon dioxide emission coefficient of heat production by gas boiler; The carbon emission constraints generated by the purchase and sale of electricity by the superior power grid are: Among them, γ grid It is the equivalent carbon dioxide emission coefficient of purchasing and selling electricity to the superior power grid.

7. The dual-objective planning method of economic benefit and low carbon performance of a park multi-energy system according to claim 1 is characterized in that: The first-stage equipment scale planning model and the second-stage optimization scheduling model are converted into a single-stage planning model based on random variables and typical scenarios, including: A stratified sampling method is adopted to take the renewable energy output and electric and thermal load scenario data as a random variable population, and the random variable population is divided into multiple levels, and samples are randomly selected in each level as random variables; wherein each level includes at least one typical scenario; Wherein, the single-stage planning model is: stAx≤b Ex+Fy i,j -Gu i,j ≤h i=1,2,...,L,j=1,2,...,n i In the formula, u i,j is the jth sample extracted from layer i; p i is the probability of the i-th layer, p i =n i / N; x is the first stage decision variable; y i,j is the second-stage decision variable corresponding to each sampling scenario u i,j ; b, h are constant vectors; A, E, F, G are constant matrices.

8. The dual-objective planning method of economic benefit and low carbon performance of a park multi-energy system according to claim 1 is characterized in that: The dual-objective planning strategy of economic benefit and low carbon performance of the multi-energy system of the target park is determined based on the flexible load constraints of the power grid and the heat network and the single-stage planning model, including: Based on the flexible load constraints of the power grid and the heat network, the economic benefit objective function of the integrated energy system operation and the carbon emission objective function of the integrated energy system in the single-stage planning model are solved to obtain the optimal solution corresponding to the economic benefit objective function of the integrated energy system operation and the optimal solution corresponding to the carbon emission objective function of the integrated energy system; Based on the optimal solution corresponding to the comprehensive energy system operation economic benefit objective function and the optimal solution corresponding to the comprehensive energy system carbon emission objective function, the comprehensive energy system operation economic benefit objective function and the comprehensive energy system carbon emission objective function are standardized to obtain standardized target values; Based on the standardized target values, a dual-objective planning strategy of economic benefits and low carbon for the multi-energy system of the target park is determined.

9. The dual-objective planning method for economic benefits and low carbon performance of a multi-energy system in a park according to claim 8 is characterized in that: The dual-objective planning strategy of economic benefit and low carbon performance of the multi-energy system of the target park is determined based on the standardized target value, including: Based on the standardized target value, multiple Pareto points are obtained; The target value on the Pareto frontier is processed positively, and the minimization index is converted into the maximization index; The positive target value is normalized, and the distance between the ideal point and the negative ideal point corresponding to the normalized target value is calculated; Determine the score of each Pareto point based on the distance between the ideal point and the negative ideal point corresponding to the standardized target value; The target value corresponding to the Pareto point with the highest score is used as the dual-objective planning strategy of economic benefit and low carbon of the multi-energy system of the target park.

10. A dual-objective planning device for economic benefits and low carbon performance of a multi-energy system in a park, characterized in that: include: The collection module is used to collect the renewable energy output and electric and thermal load scenario data of the target park, and generate flexible load constraints for the power grid and heat network; A construction module is used to establish a dual-objective collaborative planning framework based on the scenario data, wherein the dual-objective collaborative planning framework includes a first-stage equipment scale planning model determined based on the economic benefits and annual investment returns of each energy source, and a second-stage optimization scheduling model determined based on the economic benefits and carbon emissions of each energy source; A conversion module, used to convert the first-stage equipment scale planning model and the second-stage optimization scheduling model into a single-stage planning model based on random variables and typical scenarios; The planning module is used to determine the dual-objective planning strategy of economic benefits and low carbon of the multi-energy system of the target park based on the flexible load constraints of the power grid and the heat network and the single-stage planning model.

Images