Multi-energy industrial park carbon-electricity cooperative scheduling method considering carbon emission factors

Abstract

The invention discloses a multi-energy industrial park carbon-electricity cooperative scheduling method considering a carbon emission factor. The method comprises the following steps: acquiring an initial power supply curve and an initial power demand curve, and inputting the curves into a power clearing model considering a space-time carbon emission factor to obtain the space-time carbon emission factor; inputting the space-time carbon emission factor into a carbon-electricity joint decision model of the multi-energy industrial park to obtain an adjusted power demand curve and a carbon quota demand curve; and respectively inputting the adjusted power demand curve and the carbon quota demand curve into a power clearing model and a carbon clearing model which consider the space-time carbon emission factor to respectively obtain a power clearing result and a carbon clearing result, wherein the power clearing result and the carbon clearing result jointly serve as a carbon-power cooperative scheduling result. According to the method, by introducing the space-time carbon emission factor calculation model, the energy use plan can be adjusted, the production cost of the industrial park can be effectively reduced, the carbon emission of the load end is reduced, and the social welfare is improved.

Description

Technical Field

[0001] The present invention relates to a multi-energy industrial park scheduling method, and specifically to a carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors. Background Art

[0002] With the increasing global attention to carbon emission reduction, carbon-electricity collaborative scheduling has become an important means to achieve low-carbon transformation. As an important main body of energy consumption, the carbon emissions of multi-energy industrial parks mainly come from the use of electricity and natural gas. However, traditional methods mainly rely on the average carbon emission factor of the power grid in guiding the low-carbon scheduling of industrial parks, ignoring the spatio-temporal characteristics of carbon emission factors and unable to accurately reflect the real-time carbon emission situation. In addition, the existing industrial park decisions usually do not consider the coupling relationship between carbon emissions and the power system, resulting in the inability to achieve the optimal carbon-electricity joint decision. Summary of the Invention

[0003] To solve the problems existing in the background art, the present invention provides a carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The carbon-electricity collaborative scheduling method for the multi-energy industrial park of the present invention comprises the following steps:

[0006] S1. Obtain the initial power supply curve and the initial power demand curve, and then input them into the power clearing model considering spatio-temporal carbon emission factors to obtain spatio-temporal carbon emission factors;

[0007] S2. Input the spatio-temporal carbon emission factors into the carbon-electricity joint decision model of the multi-energy industrial park to obtain the adjusted power demand curve and the carbon quota demand curve;

[0008] S3. Input the adjusted power demand curve and the carbon quota demand curve into the power clearing model and the carbon clearing model considering spatio-temporal carbon emission factors respectively to obtain the power clearing result and the carbon clearing result, and the power clearing result and the carbon clearing result together serve as the carbon-electricity collaborative scheduling result.

[0009] The multi-energy industrial park mainly refers to the integration of all power-consuming equipment in the industrial park integrating multiple energy forms.

[0010] The power clearing model considering spatio-temporal carbon emission factors constructs an objective function with maximizing power social welfare as the optimization goal, and the objective function is set according to the following formula:

[0011]

[0012] Wherein, t and respectively represent the index and set of the scheduling interval, e and ε respectively represent the index and set of the multi - energy industrial park, λ e,t represents the power participation cost parameter of the multi - energy industrial park, P e,t represents the power demanded by the multi - energy industrial park, d and respectively represent the index and set of other loads excluding the multi - energy industrial park, n and respectively represent the index and set of generators, and respectively represent the power participation cost parameters of the load and the generator, and respectively represent the power dispatch quantities of the load and the generator.

[0013] The constraint conditions of the power clearing model considering spatio - temporal carbon emission factors are set according to the following formula:

[0014]

[0015]

[0016]

[0017] Among them, t and respectively represent the index and set of the scheduling interval, e and ε respectively represent the index and set of the multi - energy industrial park, λ e,t represents the power participation cost parameter of the multi - energy industrial park, P e,t represents the power demanded by the multi - energy industrial park, d and respectively represent the index and set of other loads excluding the multi - energy industrial park, n and respectively represent the index and set of generators, and respectively represent the power participation cost parameters of the load and the generator, and respectively represent the power dispatch quantities of the load and the generator, represents the maximum power flow of the line from node i to node j, i and j both represent the index of the node, θ i,t and respectively represent the phase angle of node i and the phase angle of the reference node, x ij represents the reactance, and respectively represent the minimum and maximum dual variables of the power flow of the line from node i to node j, Θ j represents the set of upstream nodes of node j, represents the power supply cost parameter at node j in the power system, and respectively represent the minimum and maximum dual variables of the power dispatch power of the generator, and respectively represent the minimum and maximum dual variables of the power dispatch power of the load, and respectively represent the minimum and maximum dual variables of the demand power of the multi - energy industrial park, and respectively represent the minimum and maximum dual variables of the phase angle of node i, represents the dual variable of the phase angle of the reference node, e j represents the spatio - temporal carbon emission factor of node j, e j0 represents substituting into the Taylor expansion point and then the preliminarily calculated spatio - temporal carbon emission factor of node j, represents the partial derivative symbol, CF represents the emission transfer allocation factor matrix, and the emission transfer allocation factor matrix is defined as the ratio between the spatio - temporal carbon emission factor and the generation emission injection amount, and ΔP e,t are respectively and P e,t -P e,t,0 , represents the expansion point selected for Taylor expansion, R j represents the element in the j - th row of the node generation emission injection amount matrix in the power system, A represents the inverse matrix of the emission transfer allocation factor matrix, T b,n represents the element in the b - th row and j - th column of the power transfer distribution factor matrix, represents the carbon emission intensity of the generator, Ψ j represents the set of devices located at node j.

[0018] The carbon - electricity coupling decision - making model of the multi - energy industrial park establishes an objective function with minimizing the carbon cost as the optimization goal, and the objective function is set according to the following formula:

[0019]

[0020] where, t and respectively represent the index and set of the scheduling interval, P e,t represents the demand power of the multi - energy industrial park, represents the power cost parameter, and respectively represent the carbon quota demand and allocation amount of the multi - energy industrial park, represents the carbon quota cost parameter, G e,t represents the demand natural gas of the multi - energy industrial park, represents the natural gas cost parameter, and respectively represent the power and heat load curtailment in the multi - energy industrial park, and represents the cost of unit reduction of electricity and heat loads in the multi - energy industrial park, represents the curtailment cost parameter of distributed new energy (wind curtailment) in the multi - energy industrial park, represents the curtailment electricity of distributed new energy in the multi - energy industrial park.

[0021] The constraint conditions of the carbon - electricity coupling decision - making model of the multi - energy industrial park are set according to the following formula:

[0022]

[0023]

[0024] Among them, b and represent the index and set of lines, and g, k, h, l, m, and s represent the indexes of combined heat and power units, distributed renewable energy generators, heat pumps, gas boilers, thermal energy storage, and electrical energy storage in the multi - energy industrial park respectively, and represent the carbon quota demand and allocation volume of the multi - energy industrial park respectively, P e,t represents the electricity demand of the multi - energy industrial park, represents the spatio - temporal carbon emission factor of the power grid where the node of the multi - energy industrial park is located, represents the carbon emission factor of the natural gas network port, represents the electricity participation cost parameter of the multi - energy industrial park, and represent the minimum and maximum electricity participation cost parameters of the multi - energy industrial park, SOC s,T and SOC s,0 represent the state of charge at the end time and the initial time of the electrical energy storage in the multi - energy industrial park, SOC m,T and SOC m,0 represent the state of charge at the end time and the initial time of the thermal energy storage in the multi - energy industrial park, G e,t represents the natural gas demand of the multi - energy industrial park, represents the carbon quota allocated from the daily initial carbon quota by the multi - energy industrial park within the scheduling interval t, represents the initial carbon quota of the multi - energy industrial park in the entire scheduling period, and represent the carbon quota demand and configuration participation cost parameters of the multi - energy industrial park respectively, G g,t and G l,t represent the natural gas consumed by the combined heat and power unit and the gas boiler in the multi - energy industrial park respectively, SOC s,t and SOC m,t represent the stored power of the electrical energy storage and the thermal energy storage in the multi - energy industrial park, and respectively represent the charging and discharging powers of the electrical energy storage in the multi - energy industrial park, and respectively represent the charging and discharging efficiencies of the electrical energy storage in the multi - energy industrial park, and respectively represent the binary variables indicating whether the electrical energy storage in the multi - energy industrial park is charging / discharging, and respectively represent the charging and discharging powers of the thermal energy storage in the multi - energy industrial park, and respectively represent the charging and discharging efficiencies of the thermal energy storage in the multi - energy industrial park, and respectively represent the binary variables indicating whether the thermal energy storage in the multi - energy industrial park is charging / discharging, represents the predicted power of the distributed renewable energy generators in the multi - energy industrial park, represents the curtailed power of the distributed renewable energy generators in the multi - energy industrial park, P h,t represents the power consumed by the heat pump in the multi - energy industrial park, and respectively represent the electrical and thermal power generation efficiencies of the combined heat and power unit in the multi - energy industrial park, and respectively represent the electrical and heat load curtailments in the multi - energy industrial park, and represents the electrical and heat loads in the multi - energy industrial park, η h and η l respectively represent the thermal power generation efficiencies of the heat pump and gas boiler in the multi - energy industrial park, represents the curtailment cost of the distributed renewable energy generators in the multi - energy industrial park.

[0025] The carbon clearing model constructs an objective function with maximizing carbon social welfare as the optimization goal, and the objective function is set according to the following formula:

[0026]

[0027] where, t and respectively represent the index and set of the scheduling interval, o, represents the index and set of other participants in carbon scheduling except the multi - energy industrial park, and represent the carbon quota requirements and configuration participation cost parameters of other participants in carbon scheduling except the multi - energy industrial park, represents the carbon quota requirements and allocation amounts of other participants in carbon scheduling except the multi - energy industrial park, e and ε respectively represent the index and set of the multi - energy industrial park, and represent the carbon quota demand and the parameter of the participation cost of allocation in the multi - energy industrial park, and represent the carbon quota demand and the allocation volume in the multi - energy industrial park.

[0028] The constraint conditions of the carbon clearing model are set according to the following formula:

[0029]

[0030]

[0031] where, t and respectively represent the index and set of the scheduling interval, o, represent the index and set of the participants other than the multi - energy industrial park participating in carbon scheduling, represent the carbon quota demand and the parameter of the participation cost of allocation of the participants other than the multi - energy industrial park participating in carbon scheduling, represent the carbon quota demand and the allocation volume of the participants other than the multi - energy industrial park participating in carbon scheduling, e and ε respectively represent the index and set of the multi - energy industrial park, represent the carbon quota cost parameter, and represent the carbon quota demand and the parameter of the participation cost of allocation in the multi - energy industrial park, and represent the carbon quota demand and the allocation volume in the multi - energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota allocation volume in the multi - energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota allocation volume in the multi - energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota allocation volume of the participants other than the multi - energy industrial park participating in carbon scheduling, respectively represent the minimum and maximum dual variables of the carbon quota allocation volume of the participants other than the multi - energy industrial park participating in carbon scheduling.

[0032] The electricity clearing result includes the electricity cost parameter, the electricity demand of the multi - energy industrial park, the electricity dispatching electricity of the load and the generator. The carbon clearing result includes the carbon quota cost parameter, the carbon quota demand and the allocation volume of the multi - energy industrial park, and the carbon quota demand and the allocation volume of the participants other than the multi - energy industrial park participating in carbon scheduling.

[0033] The beneficial effects of the present invention are:

[0034] The method of the present invention can adjust the energy usage plan, effectively reduce the production cost of the industrial park, reduce carbon emissions at the consumption end, and improve social welfare by introducing a spatio-temporal carbon emission factor calculation model. The present invention introduces spatio-temporal carbon emission factors and conducts carbon-electricity collaborative scheduling for multi-energy industrial parks, which not only improves the response ability of scheduling decisions to spatio-temporal differences in carbon emissions but also realizes a double improvement in energy utilization efficiency and carbon emission reduction benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] To better understand the present invention, the content of the present invention will be further elaborated below in conjunction with the drawings and embodiments, but the implementation manners of the present invention are not limited thereto.

[0037] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0038] The carbon-electricity collaborative scheduling method for multi-energy industrial parks includes the following steps:

[0039] S1. Obtain the initial power supply curve and initial power demand curve of the power generation side and load side in the scheduling area, and then input them into the power clearing model considering spatio-temporal carbon emission factors to obtain spatio-temporal carbon emission factors;

[0040] The power generation side includes power producers such as power plants and renewable energy power generation facilities, and the load side is power consumption facilities such as industrial production facilities and household electricity. In the present invention, the load side is divided into the load of the multi-energy industrial park and other loads.

[0041] The power clearing model considering spatio-temporal carbon emission factors constructs an objective function with maximizing power social welfare as the optimization goal, and the objective function is set according to the following formula:

[0042]

[0043] where t and respectively represent the index and set of the scheduling interval, e and ε respectively represent the index and set of the multi-energy industrial park, λ e,t represents the power participation cost parameter of the multi-energy industrial park, P e,t represents the power demanded by the multi-energy industrial park, d and respectively represent the index and set of other loads excluding the multi-energy industrial park, n and respectively represent the index and set of generators, and respectively represent the power participation cost parameters of the load and the generator, and respectively represent the power scheduling electricity of the load and the generator.

[0044] The constraint conditions of the power clearing model considering spatio-temporal carbon emission factors are set according to the following formula:

[0045]

[0046]

[0047]

[0048] Among them, t and respectively represent the index and set of the scheduling interval, e and ε respectively represent the index and set of the multi-energy industrial park, and λ e,t represents the power participation cost parameter of the multi-energy industrial park, P e,t represents the power demand of the multi-energy industrial park, d and respectively represent the index and set of other loads excluding the multi-energy industrial park, n and respectively represent the index and set of generators, and respectively represent the power participation cost parameters of the load and the generator, and respectively represent the power scheduling quantities of the load and the generator, represents the maximum power flow of the line from node i to node j, represents the set of nodes, i and j both represent the indexes of nodes, and θ i,t and respectively represent the phase angle of node i and the phase angle of the reference node, x ij represents reactance. In the present invention, (·) min ,(·) max represent the lower and upper limits of the variable (·), that is, the superscript max and the superscript min respectively represent the upper and lower limits of the meaning of the following characters, and respectively represent the minimum and maximum dual variables of the power flow of the line from node i to node j, Θ j represents the set of upstream nodes of node j, : represents introducing a dual variable associated with this equation for use when constructing the Lagrangian function or deriving the dual problem. This notation is often used in mathematics to facilitate clear expression and has no actual mathematical or physical meaning, represents the power supply cost parameter at node j in the power system, and respectively represent the minimum and maximum dual variables of the power scheduling quantity of the generator, and respectively represent the minimum and maximum dual variables of the power scheduling quantity of the load, and respectively represent the minimum and maximum dual variables of the power demand in the multi-energy industrial park, and respectively represent the minimum and maximum dual variables of the phase angle of node i, represents the dual variable of the phase angle of the reference node, e j represents the spatio-temporal carbon emission factor of node j, e j0 represents the substitution into the Taylor expansion point and the spatio-temporal carbon emission factor of node j preliminarily calculated after substitution, represents the partial derivative symbol, CF represents the emission transfer allocation factor matrix, and the emission transfer allocation factor matrix is defined as the ratio between the spatio-temporal carbon emission factor and the power generation emission injection volume, and ΔP e,t are respectively and P e,t -P e,t,0 , represents the expansion point selected for Taylor expansion, R represents the power generation emission injection volume of nodes in the power system, R j represents the element in the j-th row of the power generation emission injection volume matrix of nodes in the power system, A represents the inverse matrix of the emission transfer allocation factor matrix, T b,n represents the element in the b-th row and j-th column of the power transfer distribution factor matrix, represents the carbon emission intensity of the generator, Ψ j represents the set of devices located at node j.

[0049] S2. Input the spatio-temporal carbon emission factor into the carbon-electricity joint decision-making model of the multi-energy industrial park to obtain the adjusted power demand curve and carbon quota demand curve;

[0050] The multi-energy industrial park mainly refers to the integration of all power-consuming devices in the industrial park that integrates multiple energy forms.

[0051] The multi-energy industrial park mainly refers to the integration of industrial loads and power-consuming devices of other supporting infrastructures in the industrial park that integrates multiple energy forms, and can realize the transmission, conversion and utilization of multiple energies. It obtains electricity and natural gas from the superior power grid and natural gas network, and meets the power and heat demands through internal energy transmission and conversion devices (such as combined heat and power units, gas boilers, heat pumps, thermal energy storage, electrical energy storage and distributed renewable energy generators). In carbon emission accounting, the natural gas carbon emission in the multi-energy industrial park is obtained by multiplying the natural gas consumption by the natural gas carbon emission factor, while the electricity carbon emission is obtained by multiplying the consumed electricity by the power grid carbon emission factor, and the power grid carbon emission factor will be updated in real time. The acquisition and supply of electricity and carbon emission quotas are completed through their respective electricity and carbon clearing.

[0052] The carbon - electricity coupling decision - making model of the multi - energy industrial park establishes an objective function with minimizing the carbon cost as the optimization goal. The objective function is set according to the following formula:

[0053]

[0054] Among them, \(t\) and respectively represent the index and set of the scheduling interval. \(P\) e,t represents the electricity demand of the multi - energy industrial park, represents the electricity cost parameter, and respectively represent the carbon quota demand and allocation volume of the multi - energy industrial park, represents the electricity and carbon quota cost parameters. \(G\) e,t represents the natural gas demand of the multi - energy industrial park, represents the natural gas cost parameter, and respectively represent the electricity and heat load curtailment in the multi - energy industrial park, and represent the cost of unit curtailment of electricity and heat load in the multi - energy industrial park, represents the distributed new - energy curtailment cost parameter of abandoned wind in the multi - energy industrial park, represents the distributed new - energy curtailment electricity in the multi - energy industrial park.

[0055] The constraint conditions of the carbon - electricity coupling decision - making model of the multi - energy industrial park are set according to the following formula:

[0056]

[0057]

[0058] \(G\) e,t =G g,t +G l,t

[0059]

[0060] Among them, \(b\) and represent the index and set of the line. \(g\), \(k\), \(h\), \(l\), \(m\) and \(s\) respectively represent the indexes of the combined heat and power unit, distributed renewable - energy generator, heat pump, gas - fired boiler, heat energy storage and electrical energy storage in the multi - energy industrial park, and respectively represent the carbon quota demand and allocation volume of the multi - energy industrial park. \(P\) e,t represents the electricity demand of the multi - energy industrial park, represents the spatio - temporal carbon emission factor of the power grid where the node where the multi - energy industrial park is located, that is, \(e\) at the node where the multi - energy industrial park is located j (e∈Ψ jThe spatio-temporal carbon emission factor of the power grid where it is located, represents the carbon emission factor of the natural gas network port, represents the power participation cost parameter of the multi-energy industrial park, and represent the minimum and maximum power participation cost parameters of the multi-energy industrial park, SOC s,T and SOC s,0 represent the end-of-period and initial-state-of-charge of the electrical energy storage in the multi-energy industrial park, SOC m,T and SOC m,0 represent the end-of-period and initial-state-of-charge of the thermal energy storage in the multi-energy industrial park, G e,t represents the required natural gas of the multi-energy industrial park, represents the carbon quota allocated to the multi-energy industrial park from the daily initial carbon quota within the scheduling interval t, represents the initial carbon quota of the multi-energy industrial park over the entire scheduling period, and represent the carbon quota demand and configuration participation cost parameter of the multi-energy industrial park, G g,t and G l,t represent the natural gas consumed by the combined heat and power unit and the gas boiler in the multi-energy industrial park, SOC s,t and SOC m,t represent the power stored in the electrical energy storage and thermal energy storage in the multi-energy industrial park, and represent the charging and discharging powers of the electrical energy storage in the multi-energy industrial park, and represent the charging and discharging efficiencies of the electrical energy storage in the multi-energy industrial park, and represent the binary variables indicating whether the electrical energy storage in the multi-energy industrial park is charging / discharging, and represent the charging and discharging powers of the thermal energy storage in the multi-energy industrial park, and represent the charging and discharging efficiencies of the thermal energy storage in the multi-energy industrial park, and represent the binary variables indicating whether the thermal energy storage in the multi-energy industrial park is charging / discharging, represents the predicted power of the distributed renewable energy generator in the multi-energy industrial park, represents the curtailed power of the distributed renewable energy generator in the multi-energy industrial park, P h,t represents the power consumed by the heat pump in the multi-energy industrial park, and represent the electrical and thermal power generation efficiencies of the combined heat and power unit in the multi-energy industrial park, and respectively represent the reduction of electricity and heat loads in the multi - energy industrial park, and represent the electricity and heat loads in the multi - energy industrial park, η h and η l respectively represent the thermal power generation efficiencies of heat pumps and gas boilers in the multi - energy industrial park, represents the cost reduction of distributed renewable energy generators in the multi - energy industrial park, represents the cost per unit reduction of electricity and heat loads in the multi - energy industrial park. A node refers to the convergence point of electrical connections between all devices in the power system.

[0061] S3. Input the adjusted electricity demand curve and carbon quota demand curve into the electricity clearing model and carbon clearing model considering spatio - temporal carbon emission factors respectively to obtain the electricity clearing result and carbon clearing result. The electricity clearing result and carbon clearing result together serve as the carbon - electricity coordinated scheduling result.

[0062] The carbon clearing model constructs an objective function with maximizing carbon social welfare as the optimization goal. The objective function is set according to the following formula:

[0063]

[0064] where, t and respectively represent the index and set of the scheduling interval, o, represents the index and set of other participants in carbon scheduling except the multi - energy industrial park, and represent the carbon quota demand and configuration participation cost parameters of other participants in carbon scheduling except the multi - energy industrial park, represents the carbon quota demand and configuration quantity of other participants in carbon scheduling except the multi - energy industrial park. e and ε respectively represent the index and set of the multi - energy industrial park, and represent the carbon quota demand and configuration participation cost parameters of the multi - energy industrial park, and represent the carbon quota demand and configuration quantity of the multi - energy industrial park.

[0065] The constraint conditions of the carbon clearing model are set according to the following formula:

[0066]

[0067]

[0068] where, t and respectively represent the index and set of the scheduling interval, o, Indices and sets representing participants in carbon dispatch other than multi - energy industrial parks Carbon quota demands and participation cost parameters for configuration of participants in carbon dispatch other than multi - energy industrial parks Carbon quota demands and amounts of configuration of participants in carbon dispatch other than multi - energy industrial parks. \(e\) and \(\varepsilon\) represent the index and set of multi - energy industrial parks respectively Represents the carbon quota cost parameter and Carbon quota demands and participation cost parameters for configuration of multi - energy industrial parks and Carbon quota demands and amounts of configuration of multi - energy industrial parks Represent the minimum and maximum dual variables of the carbon quota configuration amount of multi - energy industrial parks respectively Represent the minimum and maximum dual variables of the carbon quota configuration amount of multi - energy industrial parks respectively Represent the minimum and maximum dual variables of the carbon quota configuration amount of participants in carbon dispatch other than multi - energy industrial parks respectively Represent the minimum and maximum dual variables of the carbon quota configuration amount of participants in carbon dispatch other than multi - energy industrial parks respectively

[0069] The electricity clearing result includes the electricity cost parameter The electricity demand \(P\) of the multi - energy industrial park e,t And the electricity dispatch amounts of loads and generators and The carbon clearing result includes the carbon quota cost parameter The carbon quota demand of the multi - energy industrial park and the amount of configuration And the carbon quota demands and amounts of configuration

[0070] Generally speaking, the method includes: obtaining the electricity supply curve and electricity demand curve, inputting them into the electricity clearing model considering spatio - temporal carbon emission factors to obtain spatio - temporal carbon emission factors; then, based on the released spatio - temporal carbon emission factors, establishing a two - layer carbon - electricity collaborative dispatch model for multi - energy industrial parks. The upper - layer model of the two - layer carbon - electricity collaborative dispatch model is the carbon - electricity joint decision - making model of multi - energy industrial parks, and the lower - layer model is the electricity clearing model and carbon clearing model. Transform the two - layer model into a single - layer model and perform linearization processing, transform it into a mathematical programming problem with equilibrium constraints (MPEC) for solution, solve the two - layer carbon - electricity collaborative dispatch model to obtain the carbon - electricity collaborative dispatch result, and achieve carbon - electricity collaborative dispatch

[0071] The present invention transforms a two-layer model into a single-layer model. The carbon-electricity coordinated scheduling in a multi-energy industrial park is a two-layer programming problem, which takes into account the update and release of spatio-temporal carbon emission factors in carbon scheduling and power scheduling. By replacing the clearing problem of the lower layer with its Karush-Kuhn-Tucker (KKT) conditions, this problem can be transformed into a mathematical programming problem with equilibrium constraints (MPEC), which is a mixed-integer non-linear programming problem.

[0072] In the equation the non-linear term is caused by the product of the spatio-temporal carbon emission factor and the power generation P et . The spatio-temporal carbon emission factor can be expressed through the relationship between power generation and load as:

[0073]

[0074] Then, the non-linear term is transformed into the product term of P e,t and the spatio-temporal carbon emission factor . Taking as an example, its non-linear term can be linearized by the binary expansion method.

[0075]

[0076] where K represents the number of extended segments, z k and v k are auxiliary variables in the linearization process.

[0077] In addition, the non-linear terms in the upper-layer objective function include the electricity return and the carbon emission return . These terms can be linearized by the strong duality theory. Specifically, the linearized form of the electricity return is shown in the equation:

[0078]

[0079] The linearized form of the carbon emission return is shown in the equation:

[0080]

[0081] where ε bid and ε nobid represent the sets of multi-energy industrial parks participating in scheduling and not participating in scheduling respectively. Through the above linearization process, the MPEC problem is transformed into a mixed-integer linear programming problem, which can be solved using commercial solvers.

[0082] As described above, it is only the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A carbon - electricity collaborative scheduling method for multi - energy industrial parks considering carbon emission factors, characterized in that, It includes the following steps: S1. Obtain the initial power supply curve and the initial power demand curve, and then input them into the power clearing model considering spatio-temporal carbon emission factors to obtain the spatio-temporal carbon emission factors; S2. Input the spatio-temporal carbon emission factors into the carbon-electricity joint decision-making model of the multi-energy industrial park to obtain the adjusted power demand curve and the carbon quota demand curve; S3. Input the adjusted power demand curve and the carbon quota demand curve into the power clearing model and the carbon clearing model considering spatio-temporal carbon emission factors respectively to obtain the power clearing result and the carbon clearing result, and the power clearing result and the carbon clearing result together serve as the carbon-electricity collaborative scheduling result.

2. The carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors according to claim 1, characterized in that: The multi-energy industrial park mainly refers to the integration of all power-consuming equipment in the industrial park integrating multiple energy forms.

3. The carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors according to claim 1, characterized in that: The power clearing model considering spatio-temporal carbon emission factors constructs an objective function with maximizing power social welfare as the optimization goal, and the objective function is set according to the following formula: Among them, t and respectively represent the index and set of the scheduling interval, e and respectively represent the index and set of the multi - energy industrial park, λ e,t represents the electricity participation cost parameter of the multi - energy industrial park, P e,t represents the electricity demand of the multi - energy industrial park, d and respectively represent the index and set of other loads excluding the multi - energy industrial park, n and respectively represent the index and set of generators, and respectively represent the electricity participation cost parameters of the load and the generator, and respectively represent the electricity dispatching quantities of the load and the generator.

4. A carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors according to claim 1, characterized in that: The constraint conditions of the power clearing model considering spatio-temporal carbon emission factors are set according to the following formula: Among them, t and respectively represent the index and set of the scheduling interval, e and respectively represent the index and set of the multi-energy industrial park, λ e,t represents the power participation cost parameter of the multi-energy industrial park, P e,t represents the power demand of the multi-energy industrial park, d and respectively represent the index and set of other loads excluding the multi-energy industrial park, n and respectively represent the index and set of generators, and respectively represent the power participation cost parameters of the load and the generator, and respectively represent the power scheduling electricity quantities of the load and the generator, represents the maximum power flow of the line from node i to node j. Both i and j represent the indices of nodes, θ i,t and respectively represent the phase angle of node i and the phase angle of the reference node, x ij represents the reactance, and respectively represent the minimum and maximum dual variables of the power flow of the line from node i to node j, Θ j represents the set of upstream nodes of node j, represents the power supply cost parameter at node j in the power system, and respectively represent the minimum and maximum dual variables of the power scheduling electricity quantity of the generator, and respectively represent the minimum and maximum dual variables of the power scheduling electricity quantity of the load, and respectively represent the minimum and maximum dual variables of the power demand of the multi-energy industrial park, and respectively represent the minimum and maximum dual variables of the phase angle of node i, represents the dual variable of the phase angle of the reference node, e j represents the spatio-temporal carbon emission factor of node j, e j0 represents the substitution of the Taylor expansion point and then the preliminary calculated spatio-temporal carbon emission factor of node j, represents the partial derivative symbol, CF represents the emission transfer allocation factor matrix, and the emission transfer allocation factor matrix is defined as the ratio between the spatio-temporal carbon emission factor and the power generation emission injection amount, and ΔP e,t are respectively and P e,t -P e,t,0 , represents the expansion point selected for the Taylor expansion, R j represents the j-th row element in the matrix of node generation emission injection amounts in the power system, A represents the inverse matrix of the emission transfer allocation factor matrix, T b,n represents the element in the b-th row and j-th column of the power transfer distribution factor matrix, represents the carbon emission intensity of the generator, Ψ j represents the set of devices located at node j.

5. The carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors according to claim 1, wherein: The carbon-electricity coupling decision-making model of the multi-energy industrial park establishes an objective function with minimizing carbon cost as the optimization goal, and the objective function is set according to the following formula: Among them, t and respectively represent the index and set of the scheduling interval, P e,t represents the power demand of the multi-energy industrial park, represents the power cost parameter, and respectively represent the carbon quota demand and allocation volume of the multi-energy industrial park, represents the carbon quota cost parameter, G e,t represents the natural gas demand of the multi-energy industrial park, represents the natural gas cost parameter, and respectively represent the power and heat load curtailments in the multi-energy industrial park, and represent the costs of unit curtailments of power and heat loads in the multi-energy industrial park, represents the distributed new energy curtailment cost parameter for abandoned wind in the multi-energy industrial park, represents the distributed new energy curtailment power in the multi-energy industrial park.

6. A carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors according to claim 1, characterized in that: The constraint conditions of the carbon-electricity coupling decision-making model of the multi-energy industrial park are set according to the following formula: G e,t = G g,t + G l,t Among them, b and represent the index and set of lines. g, k, h, l, m, and s respectively represent the indices of the combined heat and power unit, distributed renewable energy generator, heat pump, gas boiler, thermal energy storage, and electrical energy storage in the multi - energy industrial park. and respectively represent the carbon quota demand and allocation volume of the multi - energy industrial park. P e,t represents the electricity demand of the multi - energy industrial park. represents the spatio - temporal carbon emission factor of the power grid where the node of the multi - energy industrial park is located. represents the carbon emission factor of the natural gas network port. represents the power participation cost parameter of the multi - energy industrial park. and respectively represent the minimum and maximum power participation cost parameters of the multi - energy industrial park. SOC s,T and SOC s,0 respectively represent the state of charge at the end and initial times of the electrical energy storage in the multi - energy industrial park. SOC m,T and SOC m,0 respectively represent the state of charge at the end and initial times of the thermal energy storage in the multi - energy industrial park. G e,t represents the natural gas demand of the multi - energy industrial park. represents the carbon quota allocated to the multi - energy industrial park from the daily initial carbon quota within the scheduling interval t. represents the initial carbon quota of the multi - energy industrial park over the entire scheduling period. and respectively represent the carbon quota demand and allocation participation cost parameters of the multi - energy industrial park. G g,t and G l,t respectively represent the natural gas consumed by the combined heat and power unit and the gas boiler in the multi - energy industrial park. SOC s,t and SOC m,t represent the stored power of the electrical energy storage and the thermal energy storage in the multi - energy industrial park. and respectively represent the charging and discharging power of the electrical energy storage in the multi - energy industrial park. and respectively represent the charging and discharging efficiency of the electrical energy storage in the multi - energy industrial park. and respectively represent the binary variables indicating whether the electrical energy storage in the multi - energy industrial park is charging / discharging. and respectively represent the charging and discharging power of the thermal energy storage in the multi - energy industrial park. and respectively represent the charging and discharging efficiency of the thermal energy storage in the multi - energy industrial park. and A binary variable indicating whether the thermal energy storage in the multi - energy industrial park is charging / discharging, represents the predicted power of the distributed renewable energy generators in the multi - energy industrial park, represents the curtailed power of the distributed renewable energy generators in the multi - energy industrial park, P h,t represents the power consumed by the heat pump in the multi - energy industrial park, and represent the electricity and heat generation efficiencies of the combined heat and power unit in the multi - energy industrial park respectively, and represent the electricity and heat load curtailments in the multi - energy industrial park respectively, and represent the electricity and heat loads in the multi - energy industrial park, η h and η l represent the heat generation efficiencies of the heat pump and gas boiler in the multi - energy industrial park respectively, represents the curtailment cost of the distributed renewable energy generators in the multi - energy industrial park.

7. A carbon-electricity collaborative scheduling method for a multi-energy industrial park considering carbon emission factors according to claim 1, characterized in that: The carbon clearing model constructs an objective function with maximizing carbon social welfare as the optimization goal, and the objective function is set according to the following formula: Among them, t and respectively represent the index and set of scheduling intervals, represent the index and set of participants other than the multi-energy industrial park participating in carbon scheduling, and represent the carbon quota requirements and configuration participation cost parameters of participants other than the multi-energy industrial park participating in carbon scheduling, represent the carbon quota requirements and configured quantities of participants other than the multi-energy industrial park participating in carbon scheduling, e and respectively represent the index and set of the multi-energy industrial park, and represent the carbon quota requirements and configuration participation cost parameters of the multi-energy industrial park, and represent the carbon quota requirements and configured quantities of the multi-energy industrial park.

8. A method for collaborative carbon and electricity scheduling in a multi-energy industrial park considering carbon emission factors according to claim 1, characterized in that: The constraint conditions of the carbon clearing model are set according to the following formula: Among them, \(t\) and respectively represent the index and set of the scheduling interval, represent the index and set of the participants in carbon scheduling other than the multi-energy industrial park, represent the carbon quota demand and the configuration participation cost parameter of the participants in carbon scheduling other than the multi-energy industrial park, represent the carbon quota demand and the configured quantity of the participants in carbon scheduling other than the multi-energy industrial park, \(e\) and respectively represent the index and set of the multi-energy industrial park, represent the carbon quota cost parameter, and represent the carbon quota demand and the configuration participation cost parameter of the multi-energy industrial park, and represent the carbon quota demand and the configured quantity of the multi-energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota configured quantity of the multi-energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota configured quantity of the multi-energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota configured quantity of the participants in carbon scheduling other than the multi-energy industrial park, respectively represent the minimum and maximum dual variables of the carbon quota configured quantity of the participants in carbon scheduling other than the multi-energy industrial park.

9. The multi-energy industrial park carbon-electricity collaborative scheduling method considering carbon emission factors according to claim 1, characterized in that: The power clearing result includes the power cost parameter, the power demand of the multi-energy industrial park, the load and the power dispatching power of the generator, and the carbon clearing result includes the carbon quota cost parameter, the carbon quota demand and allocation volume of the multi-energy industrial park, and the carbon quota demand and allocation volume of other participants in carbon scheduling except the multi-energy industrial park.

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