Low-carbon optimization control method and system

Abstract

The invention discloses a low-carbon optimization control method, which comprises the following steps: acquiring related data of energy equipment and inputting the related data into an integrated energy system, constructing an IES model based on a carbon energy collaborative hub, and constructing a low-carbon optimization control model according to the IES model based on the carbon energy collaborative hub in combination with a preset objective function and constraint conditions, solving the low-carbon optimization control model by adopting an NSGA-II algorithm of an improved elite strategy to obtain an optimization control scheme; according to the method, the carbon reduction potential of each side of the source-load storage of the comprehensive energy system can be mined, carbon-energy collaborative optimization is realized, and the emission reduction effect of carbon emission is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized operation of integrated energy systems, and in particular to a low-carbon optimization control method and system. Background Art

[0002] As the "terminal" of energy interconnection, the Integrated Energy System (IES) can give full play to the complementary advantages of various heterogeneous energy sources and meet the diversified energy needs of users. Research on IES modeling and low-carbon economic dispatch can provide strong support for the efficient consumption of renewable energy and the low-carbon transformation of energy systems.

[0003] With the continuous advancement of environmental protection, carbon emission reduction has become one of the important goals of IES optimization scheduling. Most of the existing technologies construct a two-layer low-carbon scheduling model to optimize the system energy flow distribution and carbon flow distribution respectively, and solve the final low-carbon scheduling plan in an iterative manner. However, the two-layer low-carbon scheduling method optimizes user energy consumption behavior under a specific carbon flow distribution, which is difficult to reflect the characteristics of IES carbon flow distribution that changes with energy flow at all times. It does not consider the mutual influence of energy flow and carbon flow, and may also cause objective function oscillation problems, resulting in poor carbon emission reduction effects. Summary of the invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a low-carbon optimization control method that can tap the carbon reduction potential on the source, load and storage sides of the integrated energy system, achieve carbon-energy coordinated optimization, and enhance the carbon emission reduction effect.

[0005] In a first aspect, an embodiment of the present invention provides a low-carbon optimization control method, comprising:

[0006] Obtain relevant data of energy equipment and input it into the integrated energy system;

[0007] Construct an IES model based on the carbon energy synergy hub;

[0008] According to the IES model based on the carbon energy synergy hub, combined with the pre-set objective function and constraint conditions, a low-carbon optimization control model is constructed;

[0009] The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain the optimal control scheme.

[0010] Furthermore, the construction of the IES model based on the carbon energy synergy hub includes:

[0011] According to the energy hub model and carbon flow, a carbon energy synergy hub model is constructed;

[0012] According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed.

[0013] Furthermore, the expression of the carbon energy synergy hub model is:

[0014]

[0015] Among them, ρ in is the carbon flow input vector of the carbon energy synergy hub model, ρ out is the carbon flow output vector of the carbon energy synergy hub model, V in is the energy flow-carbon flow input vector of the carbon energy synergy hub model, V out is the energy flow-carbon flow output vector of the carbon energy synergy hub model, P in is the energy flow input vector of the energy hub model, L out is the energy flow output vector of the energy hub model, H is the coupling matrix of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, n is the number of energy flow output vectors, m is the number of energy flow input vectors, O n×m is an n×m-order zero matrix.

[0016] Furthermore, the IES model based on the carbon energy synergy hub model is constructed in combination with the carbon flow model of renewable energy power generation and energy storage equipment, including:

[0017] The calculations take into account the carbon flow density of renewable energy generation and energy storage equipment;

[0018] The carbon flow density and the carbon energy synergy hub model are combined to obtain an IES model based on the carbon energy synergy hub; wherein the expression of the IES model based on the carbon energy synergy hub is:

[0019]

[0020] Among them, P re is the renewable energy output vector, P es is the energy storage charge and discharge energy vector, P in is the energy flow input vector of the energy hub model, ρ re is the carbon flow density vector corresponding to the output of renewable energy, ρ es is the carbon flow density vector corresponding to the energy storage charging and discharging power, ρ in is the carbon flow input vector of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, and C re is the energy flow coupling matrix between the renewable energy output vector and the energy flow output vector of the energy hub model, C esis the energy flow coupling matrix between the energy storage charging and discharging vector and the energy flow output vector of the energy hub model, D re is the carbon flow coupling matrix between the carbon flow density vector corresponding to the renewable energy output and the carbon flow output vector of the carbon energy synergy hub model, D es is the carbon flow coupling matrix between the carbon flow density vector corresponding to the energy storage charging and discharging power and the carbon flow output vector of the carbon energy synergistic hub model, n is the number of energy flow output vectors, m is the number of energy flow input vectors, β is the number of renewable energy output vectors, γ is the number of energy storage charging and discharging vectors, O n×(m+β+γ) is a zero matrix of order n×(m+β+γ).

[0021] Furthermore, the relevant data of the energy equipment includes at least one of the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, electricity purchase carbon flow density, photovoltaic power generation forecast value, and typical daily load energy consumption data.

[0022] Furthermore, the constraint conditions include at least one of an energy flow-carbon flow balance constraint, an energy purchase constraint, an energy conversion equipment power constraint, an energy storage constraint, and a demand response constraint.

[0023] Furthermore, the NSGA-II algorithm using the improved elite strategy is used to solve the low-carbon optimization control model to obtain an optimization control scheme, including:

[0024] Dividing the constraints into equality constraints and inequality constraints;

[0025] The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain several solution vectors;

[0026] Calculating a constraint violation value of each of the solution vectors according to the equality constraint and the inequality constraint;

[0027] According to the constraint violation value, all the solution vectors are ranked and iteratively processed to obtain an optimal solution;

[0028] An optimized control scheme is obtained according to the optimal solution.

[0029] In a second aspect, an embodiment of the present invention provides a low-carbon optimization control system, including:

[0030] A data acquisition module is used to acquire relevant data of energy equipment and input it into the integrated energy system;

[0031] IES model building module, used to build the IES model based on the carbon energy synergy hub;

[0032] A low-carbon optimization control model building module is used to build a low-carbon optimization control model according to the IES model based on the carbon-energy synergy hub, combined with a preset objective function and constraint conditions;

[0033] The model solving module is used to solve the low-carbon optimization control model by adopting the NSGA-Ⅱ algorithm of the improved elite strategy to obtain the optimization control scheme.

[0034] Furthermore, the IES model construction module includes:

[0035] According to the energy hub model and carbon flow, a carbon energy synergy hub model is constructed;

[0036] According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed.

[0037] Furthermore, the relevant data of the energy equipment includes at least one of the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, electricity purchase carbon flow density, photovoltaic power generation forecast value, and typical daily load energy consumption data.

[0038] Compared with the prior art, a low-carbon optimization control method provided by an embodiment of the present invention has the following beneficial effects: by acquiring relevant data of energy equipment and inputting it into the integrated energy system; constructing an IES model based on a carbon energy collaborative hub; constructing a low-carbon optimization control model according to the IES model based on the carbon energy collaborative hub, combined with pre-set objective functions and constraints; using the NSGA-Ⅱ algorithm with an improved elite strategy to solve the low-carbon optimization control model to obtain an optimized control scheme; being able to tap the carbon reduction potential on all sides of the IES source, load and storage, realize carbon energy collaborative optimization, enhance the carbon emission reduction effect, and provide low-carbon optimization control schemes that meet different needs, thereby improving the operating economy and environmental protection of IES. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 It is a flow chart of an embodiment of a low-carbon optimization control method provided by the present invention;

[0041] Figure 2 It is a schematic diagram of the IES structure of an embodiment of a low-carbon optimization control method provided by the present invention;

[0042] Figure 3It is a schematic diagram of daily photovoltaic output and electric / heating / cooling load curves of an embodiment of a low-carbon optimization control method provided by the present invention;

[0043] Figure 4 This is a schematic diagram of real-time carbon flow density of daily externally purchased electricity in one embodiment of a low-carbon optimization control method provided by the present invention;

[0044] Figure 5 Schematic diagram of the Pareto front in different scenarios of an embodiment of a low-carbon optimization control method provided by the present invention;

[0045] Figure 6 This is a schematic diagram of energy flow-carbon flow distribution at a typical moment of IES in an embodiment of a low-carbon optimization control method provided by the present invention;

[0046] Figure 7 It is a schematic diagram of power purchase quantity of different optimization control schemes of an embodiment of a low-carbon optimization control method provided by the present invention;

[0047] Figure 8 It is a schematic diagram of energy storage charging and discharging curves of different optimization control schemes of an embodiment of a low-carbon optimization control method provided by the present invention;

[0048] Fig. 9 It is a load distribution diagram of different optimization control schemes of an embodiment of a low-carbon optimization control method provided by the present invention;

[0049] Fig.10 It is a structural schematic diagram of an embodiment of a low-carbon optimization control system provided by the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] In the first aspect, the embodiment of the present invention provides a low-carbon optimization control method, see Figure 1 , which is a flow chart of an embodiment of a low-carbon optimization control method provided by the present invention.

[0052] like Figure 1 As shown, the method comprises the following steps:

[0053] S1: Obtain relevant data of energy equipment and input it into the integrated energy system;

[0054] S2: Construct an IES model based on the carbon energy synergy hub;

[0055] S3: According to the IES model based on the carbon energy synergy hub, combined with the pre-set objective function and constraint conditions, a low-carbon optimization control model is constructed;

[0056] S4: The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain the optimal control scheme.

[0057] In the specific implementation, the relevant data of energy equipment is obtained and input into IES (integrated energy system). According to the EH model (energy hub model) and the carbon flow, the CESH model (carbon energy synergistic hub model) is constructed. The carbon flow model and the CESH model of renewable energy power generation and energy storage equipment are combined to construct an IES model based on the carbon energy synergistic hub. Then, according to the pre-set objective function and constraints, a low-carbon optimization control model is constructed. The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model to obtain the optimization control scheme.

[0058] In summary, this application obtains relevant data of energy equipment and inputs it into the integrated energy system; constructs an IES model based on a carbon energy collaborative hub; constructs a low-carbon optimization control model based on the IES model based on the carbon energy collaborative hub, combined with pre-set objective functions and constraints; uses the NSGA-Ⅱ algorithm with an improved elite strategy to solve the low-carbon optimization control model and obtain an optimized control scheme; can tap the carbon reduction potential on all sides of the IES source, load and storage, achieve carbon energy collaborative optimization, improve the carbon emission reduction effect, and provide low-carbon optimization control schemes that meet different needs, thereby improving the operating economy and environmental protection of IES.

[0059] In an optional implementation, the construction of an IES model based on a carbon energy synergy hub includes:

[0060] According to the energy hub model and carbon flow, a carbon energy synergy hub model is constructed;

[0061] According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed.

[0062] Specifically, the EH model (i.e., energy hub model) describes the mathematical relationship between energy flow input and energy flow output in a multi-energy coupling system. The EH model is shown in the following formula:

[0063]

[0064] in, is the jth energy flow output of the EH model, P i in(i=1,2,…,m) is the i-th energy flow input of the EH model, c ji is the coupling factor of the EH model, which represents the proportional relationship between the jth energy flow output and the ith energy flow input, P in is the energy flow input vector of the EH model, L out is the energy flow output vector of the EH model, and C is the coupling matrix of the EH model.

[0065] The energy flow from input to output of the EH model undergoes energy distribution and energy conversion, so the coupling matrix C in equation (1) can be decomposed as follows:

[0066]

[0067] Among them, η ji is the energy conversion efficiency, which represents the efficiency of converting the i-th energy input into the j-th energy output.

[0068] The EH model reflects the energy transmission, conversion and storage process, but it is difficult to describe the coupling relationship between the energy flow and carbon flow of the system. Therefore, this application combines the EH model with the carbon flow, that is, introduces the carbon flow into formula (1) to obtain the CESH model (i.e., carbon energy synergy hub model), as shown in the following formula:

[0069]

[0070] Among them, ρ in is the carbon flow input vector of the CESH model, ρ out is the carbon flow output vector of the CESH model, V in is the energy flow-carbon flow input vector of the CESH model, V out is the energy flow-carbon flow output vector of the CESH model, P in is the energy flow input vector of the EH model, L out is the energy flow output vector of the EH model, H is the coupling matrix of the CESH model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, n is the number of energy flow output vectors, m is the number of energy flow input vectors, O n×m is an n×m-order zero matrix

[0071] On the basis of the carbon energy synergy hub model, the IES model based on the carbon energy synergy hub model is constructed in combination with the carbon flow model of renewable energy power generation and energy storage equipment.

[0072] In an optional implementation, the IES model based on the carbon energy synergy hub model is constructed in combination with the carbon flow model of renewable energy power generation and energy storage equipment, including:

[0073] The calculations take into account the carbon flow density of renewable energy generation and energy storage equipment;

[0074] The carbon flow density and the carbon energy synergy hub model are combined to obtain an IES model based on the carbon energy synergy hub; wherein the expression of the IES model based on the carbon energy synergy hub is:

[0075]

[0076] Among them, P re is the renewable energy output vector, P es is the energy storage charge and discharge energy vector, P in is the energy flow input vector of the energy hub model, ρ re is the carbon flow density vector corresponding to the output of renewable energy, ρ es is the carbon flow density vector corresponding to the energy storage charging and discharging power, ρ in is the carbon flow input vector of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, and C re is the energy flow coupling matrix between the renewable energy output vector and the energy flow output vector of the energy hub model, C es is the energy flow coupling matrix between the energy storage charging and discharging vector and the energy flow output vector of the energy hub model, D re is the carbon flow coupling matrix between the carbon flow density vector corresponding to the renewable energy output and the carbon flow output vector of the carbon energy synergy hub model, D es is the carbon flow coupling matrix between the carbon flow density vector corresponding to the energy storage charging and discharging power and the carbon flow output vector of the carbon energy synergistic hub model, n is the number of energy flow output vectors, m is the number of energy flow input vectors, β is the number of renewable energy output vectors, γ is the number of energy storage charging and discharging vectors, O n×(m+β+γ) is a zero matrix of order n×(m+β+γ).

[0077] Specifically, the expansion form of the carbon flow coupling matrix D in formula (3) is as follows:

[0078]

[0079] Among them, α ji is the energy allocation coefficient, which indicates the proportion of the i-th energy input allocated to the j-th energy output, ζ ji is the conversion coefficient of the carbon flow rate corresponding to the j-th energy flow output and the i-th energy flow input, c ji is the coupling factor of the EH model, which represents the proportional relationship between the jth energy flow output and the ith energy flow input, P i in (i=1,2,…,m) is the i-th energy flow input of the EH model.

[0080] With the large-scale application of renewable energy and energy storage in IES (integrated energy system), the source-load-storage integration feature of IES is becoming more and more obvious. Therefore, this application combines formula (3) with the carbon flow model of renewable energy power generation and energy storage equipment to construct a CESH model containing renewable energy and energy storage to support IES low-carbon optimization control. The carbon flow model of renewable energy power generation equipment can be expressed by the following formula:

[0081]

[0082] in, is the carbon flow rate of the pth (p=1,2,…,b) renewable energy power generation equipment, is the output of the pth renewable energy power generation device, is the carbon potential of the pth renewable energy power generation equipment.

[0083] The carbon flow model of energy storage equipment can be expressed as follows:

[0084]

[0085] in, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, is the carbon flow density corresponding to the charging power at time t, is the carbon potential at the energy storage device access point at time t, is the cumulative carbon emissions of energy storage equipment at time t, is the cumulative carbon emissions of the energy storage equipment at time t+1, Δt is the time interval, W t es is the accumulated energy of the energy storage device at time t.

[0086] According to equations (5)-(7), when multiple renewable energy sources and energy storage devices are connected, the carbon flow density corresponding to the jth energy flow output is As shown below:

[0087]

[0088] in, is the ratio of the p-th renewable energy output to the j-th energy flow output, is the conversion coefficient between the carbon flow rate of the jth energy flow output and the carbon flow rate of the pth renewable energy power generation equipment, is the ratio of the charging and discharging power of the qth energy storage device allocated to the jth energy flow output, is the conversion coefficient between the carbon flow rate corresponding to the j-th energy flow output and the carbon flow rate corresponding to the charge and discharge energy of the q-th energy storage device, is the charging and discharging power of the qth energy storage device, is the carbon flow density corresponding to the qth energy storage device, is a 0-1 variable that represents the charging and discharging state of the qth energy storage device. It is 0 when charging and 1 when discharging.

[0089] The carbon flow density and the carbon energy synergy hub model are combined to obtain the IES model based on the carbon energy synergy hub, as shown in the following formula:

[0090]

[0091] Among them, P re is the renewable energy output vector, P es is the energy storage charge and discharge energy vector, P in is the energy flow input vector of the energy hub model, ρ re is the carbon flow density vector corresponding to the output of renewable energy, ρ es is the carbon flow density vector corresponding to the energy storage charging and discharging power, ρ in is the carbon flow input vector of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, and C re is the energy flow coupling matrix between the renewable energy output vector and the energy flow output vector of the energy hub model, C es is the energy flow coupling matrix between the energy storage charging and discharging vector and the energy flow output vector of the energy hub model, D re is the carbon flow coupling matrix between the carbon flow density vector corresponding to the renewable energy output and the carbon flow output vector of the carbon energy synergy hub model, D es is the carbon flow coupling matrix between the carbon flow density vector corresponding to the energy storage charging and discharging power and the carbon flow output vector of the carbon energy synergistic hub model, n is the number of energy flow output vectors, m is the number of energy flow input vectors, β is the number of renewable energy output vectors, γ is the number of energy storage charging and discharging vectors, O n×(m+β+γ) is a zero matrix of order n×(m+β+γ), where the matrix D re and the matrix D es The matrix elements of and They are shown as follows:

[0092]

[0093] For example, see Figure 2It is a typical IES system. Taking this system as an example, the system is composed of photovoltaic generators (Photovoltaic, PV), combined heat and power (Combined Heat and Power, CHP), gas boilers (Gas Boiler, GB), electric chillers (Electric Chiller, EC), absorption chillers (Absorption Chiller, AC), electric energy storage system (Electricity Storage System, ESS) and other equipment. The load types include electricity, cooling and heat loads.

[0094] According to formula (9), the IES model of the system based on the carbon energy synergy hub can be obtained as follows:

[0095]

[0096] Among them, L e,t , L h,t , L c,t are the electricity, heating and cooling loads at time t, respectively, e,t , h,t , c,t are the carbon flow densities of electricity, heat and cooling loads at time t, P e,t is the purchased power at time t, P g,t is the gas purchasing power at time t, P t pv is the PV output at time t, P t ess is the energy storage charging and discharging power at time t, ρ g is the carbon flow density of the purchased gas, ρ pv is the carbon potential of the PV unit, is the carbon potential at the energy storage access point at time t, are the proportional coefficients of the electric energy supplied at time t to the electric load and EC, are the proportion coefficients of natural gas purchased at time t allocated to CHP and GB, are the proportional coefficients of the thermal power output of GB to AC and thermal load, respectively. are the electrical efficiency and thermal efficiency of CHP, respectively, gb , η ec , η ac are the conversion efficiencies of GB, EC, and AC, respectively. is a 0-1 variable representing the charging and discharging state of the ESS at time t. It is 0 when charging and 1 when discharging. e ' ,t , P h ' ,t , P c ' ,tare auxiliary variables used to simplify the formula expression, and they satisfy the following relationships:

[0097]

[0098] In an optional embodiment, the relevant data of the energy equipment includes at least one of the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, electricity purchase carbon flow density, photovoltaic power generation forecast value, and typical daily load energy consumption data.

[0099] Specifically, collect relevant data on the economy, technology and load energy consumption of energy equipment, including the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, carbon flow density of purchased electricity, photovoltaic power generation forecast value, and typical daily load energy consumption data, and input them into IES.

[0100] In an optional embodiment, the constraint condition includes at least one of an energy flow-carbon flow balance constraint, an energy purchase constraint, an energy conversion equipment power constraint, an energy storage constraint, and a demand response constraint.

[0101] Specifically, the low-carbon optimization control model aims to minimize the operating cost and carbon emissions on the scheduling day, and its objective function is as follows:

[0102] min f1=C op +C dr (16)

[0103]

[0104] Among them, f1 is the economic cost of the dispatch day, including the operation and maintenance cost C op and demand response cost C dr , f2 is the carbon emission on the scheduling day, and T is the scheduling period.

[0105] Specifically, the operation and maintenance cost C op It is composed of electricity purchase cost, gas purchase cost and equipment maintenance cost, as shown below:

[0106]

[0107] Among them, P k,t is the operating power of device k at time t; c e,t is the electricity price in period t; c g is the natural gas price; c m,k is the maintenance cost coefficient of equipment k; c es is the maintenance cost coefficient of electric energy storage.

[0108] Demand response cost C drThis includes the cost that the IES operator needs to compensate users for the impact on energy comfort caused by load transfer, load reduction, and load substitution, as shown below:

[0109]

[0110] Among them, c tran 、c cut 、c rep are the compensation cost coefficients for load transfer, load reduction, and load substitution, respectively. are the load transfer, load reduction and load substitution at time t, are the transfer amounts of electricity, heat and cooling loads at time t, respectively. A positive value represents load transfer in, and a negative value represents load transfer out. are the reductions in electricity, heating and cooling loads, They are the replacement amounts of electricity, heat and cooling loads respectively. A positive value represents load replacement, and a negative value represents load replacement.

[0111] The low-carbon optimization control model also needs to meet given constraints, including energy flow-carbon flow balance constraints, energy purchase constraints, energy conversion equipment power constraints, energy storage constraints, and demand response constraints.

[0112] First, energy flow-carbon flow balance constraint: The low-carbon optimization control method of this application takes into account the use of demand response on the load side to participate in carbon reduction, so the energy flow output vector L of the CESH model out It is necessary to satisfy equation (9) and the following equation (23):

[0113]

[0114] in, It is the base load of electricity, heating and cooling before demand response.

[0115] Second, energy purchase constraints: Since IES is connected to the upper-level power grid and natural gas network, the transmission power between networks is constrained by the line transmission capacity:

[0116] P e,t ≤P e,cap (twenty four)

[0117] P g,t ≤P g,cap (25)

[0118] Among them, P e,cap , P g,cap They are the upper limits of the purchased electricity power and purchased gas power respectively.

[0119] Third, equipment power constraints: The operating power of the equipment is limited by its capacity and needs to satisfy the following formula:

[0120] 0≤P k,t ≤P k,cap (26)

[0121] Among them, P k,cap is the upper limit of the operating power of device k.

[0122] Fourth, energy storage constraints: Energy storage equipment can realize the cross-time transfer of energy through charging and discharging. Its cumulative energy change is related to parameters such as initial storage capacity, charging and discharging power, charging and discharging efficiency, and self-consumption rate, which can be expressed by the following formula:

[0123]

[0124] Among them, W t and W t+1 are the accumulated energy of the energy storage device at time t and t+1, σ is the self-loss coefficient of the energy storage device, η cha and η dis are the charging and discharging efficiency of the energy storage device, is a 0-1 variable representing the state of the energy storage device at time t, 0 represents charging, 1 represents discharging, It is the charging and discharging power of the energy storage device at time t. It is a positive value when charging and a negative value when discharging.

[0125] The charging and discharging power of energy storage equipment must be limited within a certain range, as shown below:

[0126] -P dis,max ≤P t ess ≤P cha,max (28)

[0127] Among them, P cha,max , P dis,max Respectively represent the maximum charging and discharging power of the energy storage device.

[0128] The accumulated energy of the energy storage device should meet the following constraints to prevent overcharging and discharging from endangering the battery life:

[0129] W min ≤W t ≤W max (29)

[0130] Among them, W min , W max They are respectively the upper and lower limits of the energy storage capacity allowed by the energy storage device.

[0131] In addition, the energy storage capacity of the energy storage device should be kept equal at the beginning and end of the dispatch cycle to maintain the charge and discharge balance of a complete dispatch cycle:

[0132] W start =W end (30)

[0133] Among them, W start and W end are the storage amounts of the energy storage equipment at the beginning and end of the scheduling cycle respectively.

[0134] Fifth, demand response constraints: the transferable load is only transferred in time, and the total load in the entire scheduling cycle needs to be kept unchanged due to load transfer. At the same time, it needs to be limited by the total transferable load. Taking the electric load as an example, it is as follows:

[0135]

[0136] Among them, c tran is the transferable load ratio coefficient of the electric load.

[0137] The load that can be reduced needs to be limited by the total amount of load that can be reduced. Taking the electric load as an example, it is as follows:

[0138]

[0139] Among them, c cut It is the load reduction ratio coefficient of the electrical load.

[0140] The replaceable load is limited by the total amount of replaceable load, taking the electric load as an example, as shown below:

[0141]

[0142] Among them, c rep is the alternative load ratio coefficient of the electric load.

[0143] In addition, replaceable loads can only replace different types of loads in the same period, and it is necessary to ensure that the sum of electricity, heating and cooling loads in the same period does not change due to energy substitution, as shown below:

[0144]

[0145] In an optional implementation, the NSGA-II algorithm using the improved elite strategy is used to solve the low-carbon optimization control model to obtain an optimization control scheme, including:

[0146] Dividing the constraints into equality constraints and inequality constraints;

[0147] The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain several solution vectors;

[0148] Calculating a constraint violation value of each of the solution vectors according to the equality constraint and the inequality constraint;

[0149] According to the constraint violation value, all the solution vectors are ranked and iteratively processed to obtain an optimal solution;

[0150] An optimized control scheme is obtained according to the optimal solution.

[0151] Specifically, the above constraints, including equation (9), equation (23)-equation (35), are first divided into equality constraints and inequality constraints. The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model to obtain several solution vectors. The divided constraints are expressed as follows:

[0152]

[0153] Among them, g v (x) is the vth equality constraint, h u (x) is the u-th inequality constraint, x is the solution vector obtained by using the NSGA-Ⅱ algorithm with the improved elite strategy, and U1 and U2 are the number of equality constraints and inequality constraints, respectively.

[0154] According to the equality constraint and the inequality constraint, the constraint violation value δ(x) of each solution vector is calculated, and the calculation formula is as follows:

[0155]

[0156] Among them, when h u When (x)≤0, <h u (x)> is 0; when h u When (x)>0, <h u (x)>=h u (x).

[0157] It can be understood that if δ(x) is 0, x is a feasible solution, and if δ(x) is not 0, x is an infeasible solution. The larger δ(x) is, the less x satisfies the constraint. All the solution vectors are ranked and iteratively processed according to the constraint violation value to obtain the optimal solution. Any feasible solution has a higher non-dominated ranking level than any infeasible solution. For infeasible solutions, individuals with smaller constraint violation values ​​have higher non-dominated ranking levels. The constrained dominance relationship can fully ensure that individuals that meet the constraints have priority to enter the next generation training link, so as to ensure that the final iterative result is the global optimal solution.

[0158] For example, Figure 2 Take the IES system in as an example, where the rated parameters of various energy equipment are shown in Table 1:

[0159] Table 1 IES equipment parameters

[0160]

[0161] The time-of-use electricity price and natural gas price are shown in Table 2 below:

[0162] Table 2 Energy prices

[0163]

[0164]

[0165] See also Figure 3 , Figure 4 The figure shows the daily photovoltaic output, load curve and real-time carbon flow density of electricity purchased from outside the system. The carbon flow density of gas purchased from outside is 0.225kgCO2·kWh -1 , the carbon potential of the PV unit is 0.033kgCO2·kWh -1 In order to verify the effectiveness of the low-carbon optimization control method of this application, the following two scenarios are set for comparative analysis:

[0166] Scenario 1: Based on the EH model, without considering demand response, IES low-carbon optimization control is performed;

[0167] Scenario 2: Based on the CESH model, taking demand response into consideration, IES low-carbon optimization control is performed.

[0168] See also Figure 5 The Pareto optimal frontiers obtained under two scenarios are shown in Figure 1. Figure 5 It can be seen that in both scenarios, as the carbon emissions of the system continue to decrease, the corresponding economic costs continue to increase, that is, there is a contradictory relationship between the economic cost and carbon emissions of the system, and the Pareto frontier solution set of scenario 2 is better than that of scenario 1, indicating that the low-carbon optimization control method of this application can provide decision makers with a better decision space in terms of economy and environmental protection. In order to further analyze the differences between different schemes in the two scenarios, Figure 5 There are four farther points in the figure, corresponding to four operation schemes, as shown in Table 3 below:

[0169] Table 3 Comparison of different optimization control schemes

[0170] plan Operation and maintenance cost / yuan Demand response cost / yuan Economic cost / yuan Carbon emissions / kg A 13102.03 -- 13102.03 15105.09 B 13963.00 -- 13963.00 14464.12 C 10858.96 176.03 11034.99 13561.81 D 12160.02 190.99 12351.01 12920.02

[0171] In scenario 1, compared with scenario A, the carbon emissions of scenario B are reduced by 640.97 kg, but the economic cost is increased by 860.97 yuan. In scenario 2, compared with scenario C, the carbon emissions of scenario D are reduced by 641.79 kg, but the economic cost is increased by 1316.02 yuan. At the same time, the economic costs and carbon emissions of scenarios C and D are lower than those of scenarios A and B. Therefore, scenario C is more suitable for IES decision makers who prefer economy, and scenario D is more suitable for IES decision makers who prefer environmental protection. You can also choose the best scenario based on your preference for economy and environmental protection. Figure 7 The Pareto front shown shows a compromise operating scenario.

[0172] See also Figure 6 , showing the energy flow and carbon flow distribution of IES corresponding to operation scheme C at two representative times: 03:00 and 12:00. The black, green and red values ​​in the figure represent the energy flow, carbon flow rate and carbon flow density corresponding to energy flow input and output, respectively, in units of kW and kgCO2·h -1 and kgCO2·kWh -1 It can be seen that since the system is at the peak of photovoltaic output at 12:00, the carbon flow density corresponding to the electrical load is low (0.054kgCO2·kWh -1 ), which means that energy is used more cleanly at this time. At 03:00, the photovoltaic output is 0, and the load demand is mainly met by purchased electricity. Therefore, the carbon flow density corresponding to the electric load is higher than that at 12:00 (0.889kgCO2·kWh -1 ); In addition, at 12:00 and 03:00, the difference between the carbon emissions input into the system and the carbon emissions output is equal to the carbon emissions stored in the ESS, which verifies that the calculation results obtained by the CESH model comply with the principle of carbon conservation.

[0173] The energy purchasing strategies of Plan C and Plan D are as follows: Figure 7As shown, it can be seen that the power purchased by Scheme C during the periods of 0:00-7:00 and 23:00-24:00 is higher than that of Scheme D, and the power purchased by Scheme C during the period of 8:00-22:00 is lower than that of Scheme D, and the power purchased by gas is higher than that of Scheme D. This is because the periods of 0:00-7:00 and 23:00-24:00 are at the low point of electricity prices, and Scheme C prefers better economy. Therefore, during the period of low electricity prices, the power purchased from the external network is increased, and the power purchased by electricity is reduced in other periods, and more economical natural gas is selected for energy supply. Scheme D prefers better environmental protection, and the carbon flow density of electricity purchased during the period of 8:00-22:00 is low. Increasing the power purchased by electricity at this time can reduce carbon emissions from electricity purchases. When the carbon flow density of electricity purchased during the periods of 0:00-7:00 and 23:00-24:00 is high, the power purchased by gas is increased to ensure energy supply. Therefore, by using the CESH model on the source side to describe the carbon emission characteristics of different energy supply modes, we can formulate economical or environmentally friendly energy supply strategies based on the preference for economy and environmental protection, through the coordination and complementarity between energy supply modes, and give full play to the carbon reduction potential on the source side.

[0174] The ESS charging and discharging strategies of Scheme C and Scheme D are as follows: Figure 8 As shown, it can be seen that Scheme C charges during the 1:00-8:00 period and discharges during the 19:00-21:00 period. This is because the ESS is charged during the low electricity price period and the stored electric energy is released during the peak electricity price period to meet the energy demand, which can reduce the cost of purchasing electricity. Scheme D is charged during the 7:00-11:00 period, when the electricity price is the usual price and the carbon flow density of purchasing electricity is low, so the relatively clean electric energy at this time is stored. The energy discharge period of Scheme D includes two parts: one is 13:00-15:00 and 19:00-21:00. This period is the peak load period and the electricity price is high. ESS releases electric energy to reduce electricity purchase; the other is 21:00-24:00. Although this period is at the low electricity price period, the carbon flow density of purchasing electricity is high. ESS releases the stored relatively clean electric energy, thereby avoiding the purchase of electricity at this time to increase carbon emissions. This shows that quantifying the changes in carbon emissions during the charging and discharging process of ESS through the CESH model can stimulate the carbon reduction potential on the load side and reduce the operation and maintenance costs and carbon emissions of the system.

[0175] In order to analyze the demand response strategies of different schemes, taking the electric load as an example, the electric load curves after demand response of scheme C and scheme D are as follows: Fig. 9As shown in the figure, since Scheme B under Scenario 1 does not consider demand response, the load curve of Scheme B is the benchmark load curve, and the load carbon flow density of Scheme B is the load carbon flow density before demand response. It can be seen that the carbon flow density of the electric load is low during the day and high at night. This is because the carbon flow density of electricity purchase is high at night, and the PV output is 0, while the PV output is at its peak at noon, which makes the carbon flow density of user electricity consumption low. Scheme C prefers economy, and its demand response strategy is mainly based on responding to electricity prices. When the electricity price is low (1:00-7:00 and 22:00-2 4:00 period) and reduce energy consumption in other periods, thereby reducing energy costs. Scheme D prefers environmental protection. In addition to reducing electricity consumption during peak load periods (13:00-15:00 and 19:00-21:00 periods), the remaining periods are mainly based on the response load carbon flow density. When the carbon flow density of the electric load is high (1:00-7:00 and 22:00-24:00 periods), electricity consumption is reduced, and when the carbon flow density is low (8:00-13:00 and 16:00-19:00 periods), electricity consumption is increased, thereby reducing the overall carbon emissions of the system. Therefore, by quantifying the carbon flow density of user energy consumption through the CESH model on the load side, we can know the differences in carbon emissions generated by users' energy consumption in different periods, stimulate the carbon reduction potential on the load side, and flexibly adjust users' energy consumption behavior according to the economic and environmental preferences of energy consumption.

[0176] In a second aspect, the present invention provides a low-carbon optimization control system. Fig.10 , which is a structural schematic diagram of an embodiment of a low-carbon optimization control system provided by the present invention.

[0177] like Fig.10 As shown, the system comprises:

[0178] A data acquisition module 21 is used to acquire relevant data of energy equipment and input it into the integrated energy system;

[0179] IES model building module 22, used to build an IES model based on the carbon energy synergy hub;

[0180] A low-carbon optimization control model construction module 23 is used to construct a low-carbon optimization control model according to the IES model based on the carbon energy synergy hub, combined with a preset objective function and constraint conditions;

[0181] The model solving module 24 is used to solve the low-carbon optimization control model by using the NSGA-II algorithm of the improved elite strategy to obtain an optimization control solution.

[0182] In an optional implementation, the IES model construction module 22 is further used to:

[0183] According to the energy hub model and carbon flow, a carbon energy synergy hub model is constructed;

[0184] According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed.

[0185] In an optional implementation, the carbon energy synergy hub model is expressed as:

[0186]

[0187] Among them, ρ in is the carbon flow input vector of the carbon energy synergy hub model, ρ out is the carbon flow output vector of the carbon energy synergy hub model, V in is the energy flow-carbon flow input vector of the carbon energy synergy hub model, V out is the energy flow-carbon flow output vector of the carbon energy synergy hub model, P in is the energy flow input vector of the energy hub model, L out is the energy flow output vector of the energy hub model, H is the coupling matrix of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, n is the number of energy flow output vectors, m is the number of energy flow input vectors, O n×m is an n×m-order zero matrix.

[0188] In an optional implementation, the IES model construction module 22 is further used to:

[0189] The calculations take into account the carbon flow density of renewable energy generation and energy storage equipment;

[0190] The carbon flow density and the carbon energy synergy hub model are combined to obtain an IES model based on the carbon energy synergy hub; wherein the expression of the IES model based on the carbon energy synergy hub is:

[0191]

[0192] Among them, P re is the renewable energy output vector, P es is the energy storage charge and discharge energy vector, P in is the energy flow input vector of the energy hub model, ρ re is the carbon flow density vector corresponding to the output of renewable energy, ρ es is the carbon flow density vector corresponding to the energy storage charging and discharging power, ρ in is the carbon flow input vector of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, and C re is the energy flow coupling matrix between the renewable energy output vector and the energy flow output vector of the energy hub model, C esis the energy flow coupling matrix between the energy storage charging and discharging vector and the energy flow output vector of the energy hub model, D re is the carbon flow coupling matrix between the carbon flow density vector corresponding to the renewable energy output and the carbon flow output vector of the carbon energy synergy hub model, D es is the carbon flow coupling matrix between the carbon flow density vector corresponding to the energy storage charging and discharging power and the carbon flow output vector of the carbon energy synergistic hub model, n is the number of energy flow output vectors, m is the number of energy flow input vectors, β is the number of renewable energy output vectors, γ is the number of energy storage charging and discharging vectors, O n×(m+β+γ) is a zero matrix of order n×(m+β+γ).

[0193] In an optional embodiment, the relevant data of the energy equipment includes at least one of the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, electricity purchase carbon flow density, photovoltaic power generation forecast value, and typical daily load energy consumption data.

[0194] In an optional embodiment, the constraint condition includes at least one of an energy flow-carbon flow balance constraint, an energy purchase constraint, an energy conversion equipment power constraint, an energy storage constraint, and a demand response constraint.

[0195] In an optional implementation, the model solving module 24 is further used for:

[0196] Dividing the constraints into equality constraints and inequality constraints;

[0197] The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain several solution vectors;

[0198] Calculating a constraint violation value of each of the solution vectors according to the equality constraint and the inequality constraint;

[0199] According to the constraint violation value, all the solution vectors are ranked and iteratively processed to obtain an optimal solution;

[0200] An optimized control scheme is obtained according to the optimal solution.

[0201] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several equivalent obvious variations and / or equivalent substitutions can be made without departing from the technical principles of the present invention. These obvious variations and / or equivalent substitutions should also be regarded as the protection scope of the present invention.

Claims

1. A low-carbon optimization control method, characterized in that: include: Obtain relevant data of energy equipment and input it into the integrated energy system; Construct an IES model based on the carbon energy synergy hub; According to the IES model based on the carbon energy synergy hub, combined with the pre-set objective function and constraint conditions, a low-carbon optimization control model is constructed; The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain the optimal control scheme.

2. The low-carbon optimization control method according to claim 1, characterized in that: The construction of the IES model based on the carbon energy synergy hub includes: According to the energy hub model and carbon flow, a carbon energy synergy hub model is constructed; According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed.

3. The low-carbon optimization control method according to claim 2, characterized in that: The expression of the carbon energy synergy hub model is: Among them, ρ in is the carbon flow input vector of the carbon energy synergy hub model, ρ out is the carbon flow output vector of the carbon energy synergy hub model, V in is the energy flow-carbon flow input vector of the carbon energy synergy hub model, V out is the energy flow-carbon flow output vector of the carbon energy synergy hub model, P in is the energy flow input vector of the energy hub model, L out is the energy flow output vector of the energy hub model, H is the coupling matrix of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, n is the number of energy flow output vectors, m is the number of energy flow input vectors, O n×m is an n×m-order zero matrix.

4. The low-carbon optimization control method according to claim 2, characterized in that: According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed, including: The calculations take into account the carbon flow density of renewable energy generation and energy storage equipment; The carbon flow density and the carbon energy synergy hub model are combined to obtain an IES model based on the carbon energy synergy hub; wherein the expression of the IES model based on the carbon energy synergy hub is: Among them, P re is the renewable energy output vector, P es is the energy storage charge and discharge energy vector, P in is the energy flow input vector of the energy hub model, ρ re is the carbon flow density vector corresponding to the output of renewable energy, ρ es is the carbon flow density vector corresponding to the energy storage charging and discharging power, ρ in is the carbon flow input vector of the carbon energy synergy hub model, C is the energy flow coupling matrix, D is the carbon flow coupling matrix, and C re is the energy flow coupling matrix between the renewable energy output vector and the energy flow output vector of the energy hub model, C es is the energy flow coupling matrix between the energy storage charging and discharging vector and the energy flow output vector of the energy hub model, D re is the carbon flow coupling matrix between the carbon flow density vector corresponding to the renewable energy output and the carbon flow output vector of the carbon energy synergy hub model, D es is the carbon flow coupling matrix between the carbon flow density vector corresponding to the energy storage charging and discharging power and the carbon flow output vector of the carbon energy synergistic hub model, n is the number of energy flow output vectors, m is the number of energy flow input vectors, β is the number of renewable energy output vectors, γ is the number of energy storage charging and discharging vectors, O n×(m+β+γ) is a zero matrix of order n×(m+β+γ).

5. The low-carbon optimization control method according to claim 1, characterized in that: The relevant data of the energy equipment includes at least one of the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, electricity purchase carbon flow density, photovoltaic power generation forecast value, and typical daily load energy consumption data.

6. The low-carbon optimization control method according to claim 1, characterized in that: The constraint conditions include at least one of an energy flow-carbon flow balance constraint, an energy purchase constraint, an energy conversion equipment power constraint, an energy storage constraint, and a demand response constraint.

7. The low-carbon optimization control method according to claim 1, characterized in that: The NSGA-Ⅱ algorithm using the improved elite strategy is used to solve the low-carbon optimization control model to obtain an optimization control scheme, including: Dividing the constraints into equality constraints and inequality constraints; The NSGA-Ⅱ algorithm with improved elite strategy is used to solve the low-carbon optimization control model and obtain several solution vectors; Calculating a constraint violation value of each of the solution vectors according to the equality constraint and the inequality constraint; According to the constraint violation value, all the solution vectors are ranked and iteratively processed to obtain an optimal solution; An optimized control scheme is obtained according to the optimal solution.

8. A low-carbon optimization control system, characterized in that: include: A data acquisition module is used to acquire relevant data of energy equipment and input it into the integrated energy system; IES model building module, used to build the IES model based on the carbon energy synergy hub; A low-carbon optimization control model building module is used to build a low-carbon optimization control model according to the IES model based on the carbon-energy synergy hub, combined with a preset objective function and constraint conditions; The model solving module is used to solve the low-carbon optimization control model by adopting the NSGA-Ⅱ algorithm of the improved elite strategy to obtain the optimization control scheme.

9. The low-carbon optimization control system according to claim 8, characterized in that: The IES model building modules include: According to the energy hub model and carbon flow, a carbon energy synergy hub model is constructed; According to the carbon energy synergy hub model, combined with the carbon flow model of renewable energy power generation and energy storage equipment, an IES model based on the carbon energy synergy hub is constructed.

10. The low-carbon optimization control system according to claim 8, characterized in that: The relevant data of the energy equipment includes at least one of the rated capacity of the equipment, conversion efficiency, time-of-use electricity price, gas price, electricity purchase carbon flow density, photovoltaic power generation forecast value, and typical daily load energy consumption data.

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