Joint management method and system for local energy and carbon emission permit

Abstract

The invention discloses a local energy and carbon emission right joint management method and system, and the method comprises the steps: firstly solving a power distribution network energy scheduling model, obtaining the power data of power generation equipment and a battery energy storage system, and then carrying out the calculation of the power data based on the power data; the method comprises the following steps: calculating local energy node carbon intensity data through a carbon emission flow model adaptive to a battery energy storage system, and solving a point-to-point energy and carbon emission permit joint transaction model by adopting a fast alternating direction multiplier algorithm based on the node carbon intensity data to obtain a carbon emission permit transaction result. And finally, feeding back the carbon emission permit transaction result to the power distribution network energy scheduling model, and performing energy optimization scheduling on the power distribution network. By adopting the method, the energy utilization efficiency can be improved, and carbon emission can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling technology, and in particular to a method and system for jointly managing local energy and carbon emission rights. Background Art

[0002] As the global low-carbon transition progresses, the power industry, as a major source of carbon emissions, faces the dual pressures of controlling greenhouse gas emissions and improving energy efficiency. The large-scale integration of distributed renewable energy (such as rooftop photovoltaics and small-scale wind power) is driving a shift in traditional electricity consumers toward becoming "prosumers"—both consuming energy and producing it through local power generation facilities. To integrate geographically adjacent distributed energy resources (DERs), local energy systems (LESs) and their supporting local energy markets (LEMs) are emerging. These systems allow prosumers to share energy through peer-to-peer (P2P) transactions, thereby improving energy efficiency and reducing grid load.

[0003] However, traditional energy management focuses primarily on power dispatch, lacking a framework that deeply integrates carbon emission rights (CERs) trading with the operation of DERs. Furthermore, while carbon trading strategies on the power generation side (such as carbon quota allocation and carbon pricing mechanisms) are mature, carbon management by demand-side prosumers (such as indirect carbon emissions quantification, carbon quota allocation, and trading) is still in its infancy, resulting in inefficient emissions reductions. Existing carbon emission flow (CEF) models primarily target traditional grid structures and fail to fully consider the dynamic impact of distributed energy storage devices such as battery energy storage systems (BESS) and electric vehicles (EVs). Furthermore, prosumers have complex load characteristics, and their operating states are highly coupled with energy trading and energy storage scheduling. Existing methods fail to fully integrate these dynamic factors, resulting in unrealized demand-side resource optimization potential and limited synergy between carbon trading and energy efficiency improvements. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for the joint management of local energy and carbon emission rights to improve energy utilization efficiency and reduce carbon emissions.

[0005] To achieve the above objectives, an embodiment of the present invention provides a method for jointly managing local energy and carbon emission rights, including:

[0006] Solve the distribution network energy dispatch model to obtain power data of power generation equipment and battery energy storage systems;

[0007] Based on the power data, calculate the carbon intensity data of the local energy node by adapting the carbon emission flow model of the battery energy storage system;

[0008] Based on the node carbon intensity data, a fast alternating direction multiplier algorithm is used to solve the point-to-point energy and carbon emission rights joint trading model to obtain the carbon emission rights trading results;

[0009] The carbon emission rights trading results are fed back to the distribution network energy scheduling model to perform energy optimization scheduling on the distribution network.

[0010] Optionally, the distribution network energy dispatch model aims to minimize the comprehensive cost including energy purchase, power generation, carbon emission quota purchase, equipment degradation, load reduction and energy loss; the constraints of the distribution network energy dispatch model include carbon quota purchase constraints, thermal power unit output constraints, power flow balance constraints, feeder capacity constraints, voltage amplitude constraints, current amplitude constraints, on-load tap-changing transformer state constraints, capacitor bank operation state constraints, battery energy storage system storage state balance constraints, and photovoltaic inverter power output constraints;

[0011] Optionally, the point-to-point energy and carbon emission rights joint trading model aims to minimize the comprehensive costs including the revenue from purchasing electricity, selling electricity, point-to-point transactions and the demand-side environment; the constraints of the point-to-point energy and carbon emission rights joint trading model include energy balance constraints, constant temperature control load power constraints, battery energy storage system energy storage state balance constraints, battery energy storage system charging and discharging power constraints and electric vehicle charging and discharging power constraints.

[0012] Optionally, the objective function of the distribution network energy scheduling model is:

[0013]

[0014] Where t represents time, i represents the i-th grid node in the distribution network, and Ω ST represents the set of substations, λ t ELE represents the wholesale market electricity price at time t, represents the power output of the thermal power unit at the grid node i at time t, Δt represents the time interval, Ω G represents the set of thermal power units, a i 、b i and c i represents the fuel cost coefficient of the thermal power unit in the grid node i, λ t CAR represents the carbon price at time t, represents the number of carbon quotas purchased by the thermal power unit at grid node i at time t, Ω BESSrepresents the set of battery energy storage systems, τ is the cost coefficient of battery life degradation, represents the discharge power of the battery energy storage system at grid node i at time t, Indicates the leakage loss coefficient of the battery, represents the energy storage state of the battery energy storage system at grid node i at time t, χ represents the value of the reliability cost, represents the load reduction of grid node i at time t, j represents other grid nodes connected to transmission grid node i, Ω B represents the set of feeders, I ij,t represents the square value of the current at time t on the line between grid nodes i and j, r ij Represents the resistance value of the feeder between grid node i and node j.

[0015] Optionally, the constraints of the distribution network energy scheduling model include:

[0016]

[0017]

[0018] (V i min ) 2 ≤v i,t ≤(V i max ) 2 ;

[0019]

[0020] in, represents the carbon quota initially allocated to the thermal power unit at the grid node i, represents the carbon quota purchased at grid node i, represents the carbon intensity of thermal power units at grid node i at time t, P i G,min represents the minimum power output of the thermal power unit at the grid node i, P i G,max represents the maximum power output of the thermal power unit at grid node i, k represents the grid nodes other than grid nodes i and j, Ω represents the set of distribution network nodes, represents the active power flow at time t on the line between grid node j and node k, represents the active power flow at time t on the line between grid node i and node j, represents the output of the photovoltaic system at grid node j at time t, represents the discharge power of the battery energy storage system at grid node j at time t, represents the charging power of the battery energy storage system at grid node j at time t, represents the active load of grid node j at time t, represents the active load reduction of grid node j at time t, represents the reactive power flow at time t on the line between grid node j and node k, represents the reactive power flow at time t on the line between grid node i and node j, x ij represents the reactance of the feeder between grid nodes i and j, represents the reactive power output of the capacitor bank at grid node j at time t, represents the output power of the photovoltaic inverter at grid node j at time t, represents the reactive load of grid node j at time t, represents the reactive load reduction of grid node j at time t, represents the capacity of the feeder between grid nodes i and j, v j,t represents the square value of the voltage of grid node j at time t, v i,t Represents the square value of the voltage of grid node i at time t, V i min Indicates the minimum square voltage value of grid node i, V i max represents the maximum square value of the voltage at grid node i, represents the minimum square value of the current on the line between grid node i and node j, represents the maximum square value of the current on the line between grid node i and node j, v 1,t represents the reference node voltage at time t, It represents the square value of the primary side voltage of the on-load tap-changing transformer, r min Indicates the minimum resistance value on the grid node line, tap t Indicates the state of the on-load tap-changing transformer at time t, ΔV T is the single-step voltage adjustment value, tap t+1 Indicates the state of the on-load tap-changing transformer at time t+1, tap max Indicates the maximum adjustable state of the on-load tap-changing transformer, c i,x,t represents the state of capacitor bank x at grid node i at time t, c i,x,t+1 represents the state of capacitor bank x at grid node i at time t+1, represents the maximum capacity of the capacitor bank x on the grid node i, represents the reactive power provided by the capacitor bank at grid node i at time t, It represents the capacitor bank set on the grid node i, q i,x,trepresents the reactive power output of capacitor bank x at grid node i at time t, represents the charging power of the battery energy storage system at grid node i at time t, P i CH,max represents the maximum charging power of the battery energy storage system at grid node i at time t, represents the discharge power of the battery energy storage system at grid node i at time t, P i DS,max represents the maximum discharge power of the battery energy storage system at grid node i at time t, represents the energy storage state of the battery energy storage system at grid node i at time t+1, η CH represents the charging efficiency of the battery energy storage system, η DS Indicates the charging efficiency of the battery energy storage system, represents the minimum value of the battery energy storage system energy at grid node i, represents the maximum energy of the battery energy storage system at grid node i, represents the output power of the photovoltaic inverter at grid node i at time t, represents the photovoltaic system capacity of grid node i, represents the output of the photovoltaic system at grid node i at time t.

[0021] Optionally, the carbon emission flow model adapted to the battery energy storage system considers the charging, discharging, and idle states of the battery energy storage system, and quantifies the indirect carbon emissions generated by prosumers by calculating the node carbon intensity;

[0022] The carbon emission flow model of the adapted battery energy storage system is:

[0023]

[0024] in, represents the node strength of grid node i at time t, represents the power output of the thermal power unit at grid node i at time t, represents the node carbon intensity of thermal power unit at grid node i, represents the discharge power of the battery energy storage system at grid node i at time t, represents the energy storage carbon potential of grid node i at time t, represents the set of transmission lines transmitted to grid node i, b represents the b-th branch transmission line, represents the power flow of branch b at time t, represents the branch strength of the b-th branch at time t, represents the output of the photovoltaic system at grid node i at time t.

[0025] Optionally, the objective function of the peer-to-peer energy and carbon emission rights joint trading model is:

[0026]

[0027] Where t represents time, Δt represents the time interval, m and n represent producers and consumers, Ω P represents the set of P2P transaction producers and consumers, Ω P \m represents the set of P2P transaction producers and consumers except producer and consumer m, λ t pur,ut represents the price at which the prosumer purchases electricity from the grid at time t, represents the electricity purchased by prosumer m from the grid at time t, represents the price at which the prosumer sells electricity to the grid at time t, represents the electricity sold by prosumer m to the grid at time t, d mn The electrical distance between prosumer m and prosumer n, represents the P2P transaction power between prosumer m and prosumer n, λ t CAR represents the carbon price at time t, Represents the carbon emission rights trading volume between prosumer m and prosumer n.

[0028] Optionally, the constraints of the peer-to-peer energy and carbon emission rights joint trading model include:

[0029]

[0030] When t≤T arrive hour;

[0031]

[0032] in, represents the charging power of the electric vehicle of prosumer m at time t, represents the electricity sold by prosumer m to the grid at time t, represents the P2P transaction power between prosumer m and prosumer n, represents the battery energy storage system charging power of prosumer m at time t, represents the discharge power of the battery energy storage system of prosumer m at time t, represents the power of the thermostatically controlled load of the prosumer m at time t, represents the charging power of the electric vehicle of prosumer m at time t, represents the electric vehicle discharge power of prosumer m at time t, represents the load of prosumer m at time t, represents the renewable energy generation of prosumer m at time t; represents the indoor temperature of prosumer m at time t, represents the indoor temperature of prosumer m at time t+1, Indicates that R m represents the thermal resistance of the prosumer m, T t out represents the outdoor temperature at time t, represents the minimum value of the outdoor temperature of the prosumer m, represents the maximum outdoor temperature of prosumer m, represents the minimum power of the thermostatically controlled load of prosumer m, represents the maximum power of the thermostatically controlled load of prosumer m; represents the energy storage state of the battery energy storage system of prosumer m at time t+1, represents the energy storage state of the battery energy storage system of prosumer m at time t, represents the charging power of the battery energy storage of prosumer m, represents the discharge power of the battery energy storage of prosumer m, represents the minimum energy value of the battery energy storage system of prosumer m, represents the maximum energy of the battery energy storage system of prosumer m, represents the maximum value of the battery charge and discharge power of prosumer m, represents the maximum value of the battery charge and discharge power of prosumer m, represents the battery charging power or discharging power of prosumer m at time t; represents the energy storage state of the electric vehicle of prosumer m at time t, represents the energy storage state of the electric vehicle of prosumer m at time t+1, represents the charging power of the electric vehicle of prosumer m at time t, represents the electric vehicle discharge power of prosumer m at time t, represents the minimum energy storage state of the electric vehicle of prosumer m, represents the maximum energy storage state of the electric vehicle of prosumer m, represents the minimum charging and discharging power of the electric vehicle of prosumer m, represents the maximum charge and discharge power of the electric vehicle of prosumer m, T arrive represents the arrival time of the electric vehicle, The electric vehicle of prosumer m arrives at time T arrive The energy storage state, represents the initial energy storage state of the electric vehicle of prosumer m upon arrival, represents the expected energy storage state of the electric vehicle of prosumer m when it leaves; represents the carbon emission rights trading volume at time t when prosumer m sells carbon emission rights to prosumer n, represents the carbon emission rights trading volume at time t when prosumer n sells carbon emission rights to prosumer m, represents the initial allocation of carbon quotas to the thermal power units of prosumer m, represents the electricity purchased by prosumer m from the grid at time t, represents the node strength at time t.

[0033] Optionally, based on the node carbon intensity data, a fast alternating direction multiplier algorithm is used to solve the point-to-point energy and carbon emission rights joint trading model to obtain a carbon emission rights trading result, including:

[0034] Decomposing the peer-to-peer energy and carbon emission rights joint trading model into energy trading sub-problems, peer-to-peer trading sub-problems, and carbon emission rights trading sub-problems;

[0035] The fast alternating direction multiplier method is used to iteratively update the variables and Lagrange multipliers in the energy trading subproblem, the peer-to-peer trading subproblem, and the carbon emission rights trading subproblem, narrowing the gap between the objective function and the constraints.

[0036] When the gap is smaller than a set threshold, the iteration is stopped to obtain the final carbon emission rights trading result.

[0037] To achieve the above objectives, an embodiment of the present invention further provides a local energy and carbon emission rights joint management system, including:

[0038] The distribution network energy dispatch module is used to solve the distribution network energy dispatch model and obtain the power data of the power generation equipment and battery energy storage system;

[0039] a carbon emission tracking module, configured to calculate carbon intensity data of local energy nodes based on the power data by adapting a carbon emission flow model of the battery energy storage system;

[0040] A carbon emission rights trading module is used to solve a point-to-point energy and carbon emission rights joint trading model based on the node carbon intensity data using a fast alternating direction multiplier algorithm to obtain a carbon emission rights trading result;

[0041] The distribution network energy optimization scheduling module is used to feed back the carbon emission rights trading results to the distribution network energy scheduling model to optimize the energy scheduling of the distribution network.

[0042] Compared to existing technologies, the present invention provides a method and system for the joint management of local energy and carbon emission rights. First, power data from power generation equipment and battery energy storage systems is obtained by solving the distribution network energy dispatch model, providing accurate basic information for subsequent energy management. This information can be used to rationally schedule the operation of power generation and energy storage equipment, improving energy utilization efficiency. Furthermore, using a carbon emission flow model adapted to the battery energy storage system, carbon intensity data for local energy nodes is calculated based on the power data, achieving precise quantification of carbon emissions. This helps accurately understand the carbon emissions of each node and provides a reliable basis for carbon emission rights trading and management. A fast alternating direction multiplier algorithm is then used to solve the point-to-point energy and carbon emission rights joint trading model to obtain carbon emission rights trading results. This process can incentivize market participants to participate in carbon trading, promote the rational allocation of carbon emission rights, encourage proactive emission reductions by all parties, and help achieve carbon reduction targets. Finally, the carbon emission rights trading results are fed back into the distribution network energy dispatch model, forming a closed-loop optimization mechanism that adjusts energy dispatch based on carbon trading results, ensuring that energy distribution is more consistent with the requirements of a low-carbon economy, further optimizing energy utilization in the distribution network, and reducing energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings used in the implementation methods. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a flow chart of a method for joint management of local energy and carbon emission rights provided by an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the power structure of an IEEE 30-node system provided by an embodiment of the present invention;

[0046] Figure 3 1 is a schematic diagram of the scheduling results of micro-generators in a distribution network provided by an embodiment of the present invention;

[0047] Figure 4 is a carbon intensity diagram of a battery energy storage system provided by an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the carbon intensity of a node provided by an embodiment of the present invention;

[0049] Figure 6 Schematic diagram of the evolution of peer-to-peer transactions provided by an embodiment of the present invention;

[0050] Figure 7 Schematic diagram of the scheduling results of the battery energy storage system provided by an embodiment of the present invention;

[0051] Figure 8 Schematic diagram of the scheduling result of the constant temperature control load provided by an embodiment of the present invention;

[0052] Figure 9 Schematic diagram of the dispatching results of electric vehicles provided by an embodiment of the present invention;

[0053] Figure 10 This is a schematic diagram of the carbon emission rights trading results of electric vehicles provided by an embodiment of the present invention;

[0054] Figure 11 This is a schematic diagram of energy purchasing and selling behavior of a prosumer in the following scenario provided by an embodiment of the present invention;

[0055] Figure 12 This is a schematic diagram of energy purchasing and selling behavior of prosumers in Scenario 2 provided by an embodiment of the present invention;

[0056] Figure 13 This is a schematic diagram of energy purchasing and selling behavior of a prosumer under scenario three provided by an embodiment of the present invention;

[0057] Figure 14 This is a structural block diagram of a local energy and carbon emission rights joint management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] See also Figure 1 , Figure 1 : This is a flow chart of a method for jointly managing local energy and carbon emission rights provided by an embodiment of the present invention. The method for jointly managing local energy and carbon emission rights includes steps S1 to S4:

[0060] Step S1: Solve the distribution network energy dispatch model to obtain power data of power generation equipment and battery energy storage system;

[0061] Step S2: Based on the power data, calculate the carbon intensity data of the local energy node by adapting the carbon emission flow model of the battery energy storage system;

[0062] Step S3: Based on the node carbon intensity data, a fast alternating direction multiplier algorithm is used to solve the point-to-point energy and carbon emission rights joint trading model to obtain a carbon emission rights trading result;

[0063] Step S4: Feedback the carbon emission rights trading results to the distribution network energy dispatch model to perform energy optimization dispatch on the distribution network.

[0064] For example, a pre-built distribution network energy dispatch model is solved using historical electricity consumption data, using genetic algorithms and other methods to obtain power data for each power generation device and battery energy storage system. This power data is then substituted into a pre-built carbon emission flow model adapted for the battery energy storage system to calculate the carbon intensity data for local energy nodes. Carbon emission quotas are then calculated based on the carbon intensity data for local energy nodes. A pre-built point-to-point energy and carbon emission rights trading model is then solved based on the carbon emission quotas to obtain the carbon emission rights trading results. Finally, the carbon emission rights trading results are fed back into the distribution network energy dispatch model to perform distribution network energy dispatch. For example, because prosumer A has purchased carbon emission rights, the energy management center can appropriately reduce its gas-fired power supply in subsequent energy dispatches, prioritizing battery energy storage systems and photovoltaic power generation to reduce its future carbon emissions. For residential users who sell carbon emission rights and electricity, the battery energy storage system's power supply strategy can be optimized in subsequent dispatches to encourage them to continue improving energy efficiency and reducing carbon emissions. By continuously feeding back transaction results and adjusting energy scheduling strategies, joint optimal management of local energy and carbon emission rights can be achieved.

[0065] In an optional embodiment, the objective function minimization of the distribution network energy dispatch model includes the cost of purchasing energy from the wholesale market, the cost of power generation, the cost of purchasing carbon emission quotas, the cost of equipment degradation, the cost of load reduction, and the cost of energy loss;

[0066] It can be understood that as the physical carrier connecting energy production (such as thermal power units, photovoltaics), battery energy storage systems (BESS), and consumption (prosumer loads), the distribution network's scheduling directly determines the energy's "production-transmission-consumption" path.

[0067] Specifically, the objective function of the distribution network energy dispatch model is:

[0068]

[0069] Where t represents time, i represents the i-th grid node in the distribution network, and Ω ST represents the set of substations, λ t ELE represents the wholesale market electricity price at time t, represents the power output of the thermal power unit at the grid node i at time t, Δt represents the time interval, Ω G represents the set of thermal power units, a i 、b i and c irepresents the fuel cost coefficient of the thermal power unit in the grid node i, λ t CAR represents the carbon price at time t, represents the number of carbon quotas purchased by the thermal power unit at grid node i at time t, Ω BESS represents the set of Battery Energy Storage Systems (BESS), τ is the cost coefficient of battery life degradation, represents the discharge power of the battery energy storage system at grid node i at time t, Indicates the leakage loss coefficient of the battery, represents the energy storage state of the battery energy storage system at grid node i at time t, χ represents the value of the reliability cost, represents the load reduction of grid node i at time t, j represents other grid nodes connected to transmission grid node i, Ω B represents the set of feeders, I ij,t represents the square value of the current at time t on the line between grid nodes i and j, r ij represents the resistance value of the feeder between grid node i and node j;

[0070] The constraints of the distribution network energy dispatch model include carbon quota purchase constraints, thermal power unit output constraints, power flow balance constraints, feeder capacity constraints, voltage amplitude constraints, current amplitude constraints, on-load tap-changing transformer state constraints, capacitor bank operation state constraints, battery energy storage system storage state balance constraints, and photovoltaic inverter power output constraints.

[0071] Specifically, the constraints of the distribution network energy dispatch model include:

[0072]

[0073]

[0074] (V i min ) 2 ≤v i,t ≤(V i max ) 2 ; (8)

[0075]

[0076] in, represents the carbon quota initially allocated to the thermal power unit at the grid node i, represents the carbon quota purchased at grid node i, represents the carbon intensity of thermal power units at grid node i at time t, Pi G,min represents the minimum power output of the thermal power unit at the grid node i, P i G,max represents the maximum power output of the thermal power unit at grid node i, k represents the grid nodes other than grid nodes i and j, Ω represents the set of distribution network nodes, represents the active power flow at time t on the line between grid node j and node k, represents the active power flow at time t on the line between grid node i and node j, represents the output of the photovoltaic system at grid node j at time t, represents the discharge power of the battery energy storage system at grid node j at time t, represents the charging power of the battery energy storage system at grid node j at time t, represents the active load of grid node j at time t, represents the active load reduction of grid node j at time t, represents the reactive power flow at time t on the line between grid node j and node k, represents the reactive power flow at time t on the line between grid node i and node j, x ij represents the reactance of the feeder between grid nodes i and j, represents the reactive power output of the capacitor bank at grid node j at time t, represents the output power of the photovoltaic inverter at grid node j at time t, represents the reactive load of grid node j at time t, represents the reactive load reduction of grid node j at time t, represents the capacity of the feeder between grid nodes i and j, v j,t represents the square value of the voltage of grid node j at time t, v i,t Represents the square value of the voltage of grid node i at time t, V i min Indicates the minimum square voltage value of grid node i, V i max represents the maximum square value of the voltage at grid node i, represents the minimum square value of the current on the line between grid node i and node j, represents the maximum square value of the current on the line between grid node i and node j, v 1,t represents the reference node voltage at time t, v t Base represents the square value of the primary side voltage of the on-load tap changer (OLTC), r minIndicates the minimum resistance value on the grid node line, tap t Indicates the state of the on-load tap changer (OLTC) at time t, ΔV T is the single-step voltage adjustment value, tap t+1 Indicates the state of the on-load tap-changing transformer at time t+1, tap max Indicates the maximum adjustable state of the on-load tap-changing transformer, c i,x,t represents the state of capacitor bank x at grid node i at time t, c i,x,t+1 represents the state of capacitor bank x at grid node i at time t+1, represents the maximum capacity of the capacitor bank x on the grid node i, represents the reactive power provided by the capacitor bank at grid node i at time t, It represents the capacitor bank set on the grid node i, q i,x,t represents the reactive power output of capacitor bank x at grid node i at time t, represents the charging power of the battery energy storage system at grid node i at time t, P i CH,max represents the maximum charging power of the battery energy storage system at grid node i at time t, represents the discharge power of the battery energy storage system at grid node i at time t, P i DS,max represents the maximum discharge power of the battery energy storage system at grid node i at time t, represents the energy storage state of the battery energy storage system at grid node i at time t+1, η CH represents the charging efficiency of the battery energy storage system, η DS Represents the charging efficiency of the battery energy storage system, E i BESS,min represents the minimum energy value of the battery energy storage system at grid node i, E i BESS,max represents the maximum energy of the battery energy storage system at grid node i, represents the output power of the photovoltaic inverter at grid node i at time t, S i PV represents the photovoltaic system capacity of grid node i, represents the output of the photovoltaic system at grid node i at time t.

[0077] It should be noted that formula (2) is the carbon quota purchase constraint, which means that the sum of the initial allocation quota and the purchase quota of the thermal power unit should be greater than its carbon emissions; formula (3) is the output constraint of the thermal power unit; formulas (4) and (5) are the power flow balance constraints of active power and reactive power; formula (6) is the feeder capacity constraint; formula (7) is used to calculate the voltage amplitude; formula (8) constrains the maximum and minimum values ​​of the voltage amplitude; formula (9) constrains the current amplitude; formula (10) defines the voltage of the reference node; formula (11) constrains the operating state of the OLTC; formula (12) constrains the operating state of the capacitor bank; formula (13) calculates the reactive power output of the capacitor bank; formula (14) is the maximum charge and discharge power of the BESS; formula (15) is the energy storage state balance constraint of the BESS; formula (16) is the maximum energy storage state constraint of the BESS. Formula (17) constrains the reactive power output of the photovoltaic inverter.

[0078] In step S2, it should be noted that the traditional carbon emission flow (CEF) model is based on the principle of proportional allocation and can be used to track the carbon footprint of the electricity consumption process according to the power flow from the power generation side to the demand side. Therefore, the carbon emissions of end users can be calculated through the CEF model.

[0079] Based on the power flow model, the node strength in the traditional CEF model can be calculated by the following formula:

[0080]

[0081] in, represents the node strength of grid node i at time t, represents the power output of the thermal power unit at grid node i at time t, represents the node carbon intensity of thermal power unit at grid node i, represents the set of transmission lines transmitted to grid node i, b represents the b-th branch transmission line, represents the power flow of branch b at time t, represents the branch strength of the b-th branch at time t, z∈Ω R is represented as the set of renewable energy sources, represents the power obtained by grid node i from renewable energy z at time t (i.e., the power injected by renewable energy into node i);

[0082] To clarify, nodal carbon intensity is a measure of the carbon intensity of a specific location, or "node," in the electricity grid. It takes into account the carbon intensity of the electricity production mix at that node. Carbon intensity measures the amount of carbon dioxide (CO2) emissions generated per unit of energy consumed.

[0083] However, traditional CEF models fail to consider the impact of BESS on the power system. Charging and discharging behavior complicates carbon footprint tracking. BESS has three operating states: charging, discharging, and idle. The carbon intensity of a BESS remains unchanged during idle and discharging. The carbon intensity of a BESS changes only during charging and depends on the carbon intensity of the energy absorbed.

[0084] To better integrate BESS into the system, the embodiment of the present invention adapts the traditional CEF model and proposes a formula to describe the carbon intensity evolution of BESS nodes:

[0085]

[0086] in, represents the energy storage carbon potential of grid node i at time t+1, represents the energy storage carbon potential of grid node i at time t, represents the energy storage state of the battery energy storage system at grid node i at time t, represents the node strength of grid node i at time t, represents the charging power of the battery energy storage system at grid node i at time t, and Δt represents the time interval.

[0087] Therefore, in an optional embodiment, the carbon emission flow model of the adapted battery energy storage system is:

[0088]

[0089] in, represents the node strength of grid node i at time t, represents the power output of the thermal power unit at grid node i at time t, represents the node carbon intensity of thermal power unit at grid node i, represents the discharge power of the battery energy storage system at grid node i at time t, represents the energy storage carbon potential of grid node i at time t, represents the set of transmission lines transmitted to grid node i, b represents the b-th branch transmission line, represents the power flow of branch b at time t, represents the branch strength of the b-th branch at time t, represents the output of the photovoltaic system at grid node i at time t.

[0090] In step S3, it should be noted that in the local community, in addition to purchasing electricity from the grid, prosumers can also trade energy with each other in the P2P market. These prosumers own BESS, electric vehicles (EVs), and thermostatically controlled loads (TCLs).

[0091] Therefore, a point-to-point energy and carbon emission rights joint trading model can be constructed with the goal of minimizing the total energy transaction cost.

[0092] In an optional embodiment, the peer-to-peer energy and carbon emission rights joint trading model includes minimizing the cost of purchasing electricity from the grid, the revenue from selling electricity to the grid, the P2P transaction cost, and the environmental cost on the demand side.

[0093] Specifically, the objective function of the P2P transaction between producers and consumers, that is, the objective function of the peer-to-peer energy and carbon emission rights joint trading model is:

[0094]

[0095] Where t represents time, Δt represents the time interval, m and n represent producers and consumers, Ω P represents the set of P2P transaction producers and consumers, Ω P \m represents the set of P2P transaction producers and consumers except producer and consumer m, λ t pur,ut represents the price at which the prosumer purchases electricity from the grid at time t, represents the electricity purchased by prosumer m from the grid at time t, λ t sell,ut represents the price at which the prosumer sells electricity to the grid at time t, represents the electricity sold by prosumer m to the grid at time t, d mn The electrical distance between prosumer m and prosumer n, represents the P2P transaction power between prosumer m and prosumer n, λ t CAR represents the carbon price at time t, Represents the carbon emission rights trading volume between prosumer m and prosumer n.

[0096] The constraints of the peer-to-peer energy and carbon emission rights joint trading model include energy balance constraints, constant temperature control load power constraints, battery energy storage system energy storage state balance constraints, battery energy storage system charge and discharge power constraints, and electric vehicle charge and discharge power constraints;

[0097] Specifically, the constraints of the peer-to-peer energy and carbon emission rights joint trading model include:

[0098]

[0099]

[0100] When t≤T arrive Time; (36)

[0101]

[0102] in, represents the charging power of the electric vehicle of prosumer m at time t, represents the electricity sold by prosumer m to the grid at time t, represents the P2P transaction power between prosumer m and prosumer n, represents the battery energy storage system charging power of prosumer m at time t, represents the discharge power of the battery energy storage system of prosumer m at time t, represents the power of the thermostatically controlled load of the prosumer m at time t, represents the charging power of the electric vehicle of prosumer m at time t, represents the electric vehicle discharge power of prosumer m at time t, represents the load of prosumer m at time t, represents the renewable energy generation of prosumer m at time t; represents the indoor temperature of prosumer m at time t, represents the indoor temperature of prosumer m at time t+1, Represents the thermal work parameter, R m represents the thermal resistance of the prosumer m, T t out represents the outdoor temperature at time t, represents the minimum value of the outdoor temperature of the prosumer m, represents the maximum outdoor temperature of prosumer m, represents the minimum power of the temperature control loads (TCLs) of prosumer m, represents the maximum power of the thermostatically controlled load of prosumer m; represents the energy storage state of the battery energy storage system of prosumer m at time t+1, represents the energy storage state of the battery energy storage system of prosumer m at time t, represents the charging power of the battery energy storage of prosumer m, represents the discharge power of the battery energy storage of prosumer m, represents the minimum energy value of the battery energy storage system of prosumer m, represents the maximum energy of the battery energy storage system of prosumer m, represents the maximum value of the battery charge and discharge power of prosumer m, represents the maximum value of the battery charge and discharge power of prosumer m, represents the battery charging power or discharging power of prosumer m at time t;

[0103] represents the energy storage state of the electric vehicle of prosumer m at time t, represents the energy storage state of the electric vehicle of prosumer m at time t+1, represents the charging power of the electric vehicle of prosumer m at time t, represents the electric vehicle discharge power of prosumer m at time t, represents the minimum energy storage state of the electric vehicle of prosumer m, represents the maximum energy storage state of the electric vehicle of prosumer m, represents the minimum charging and discharging power of the electric vehicle of prosumer m, represents the maximum charge and discharge power of the electric vehicle of prosumer m, T arrive represents the arrival time of the electric vehicle, The electric vehicle of prosumer m arrives at time T arrive The energy storage state, represents the initial energy storage state of the electric vehicle of prosumer m upon arrival, represents the expected energy storage state of the electric vehicle of prosumer m when it leaves;

[0104] represents the carbon emission rights trading volume at time t when prosumer m sells carbon emission rights to prosumer n, represents the carbon emission rights trading volume at time t when prosumer n sells carbon emission rights to prosumer m, represents the initial allocation of carbon quotas to the thermal power units of prosumer m, represents the electricity purchased by prosumer m from the grid at time t, represents the node strength at time t.

[0105] It should be noted that formula (23) is an energy balance constraint, which means that energy demand should be equal to energy supply; formula (24) means that in P2P transactions, the power transferred from the mth prosumer to the nth prosumer is equal to the power received by the nth prosumer from the mth prosumer; considering thermal inertia, the thermodynamic characteristics of TCLs can be expressed as formulas (25) to (27), where formula (25) describes the evolution of indoor temperature, formula (26) describes the minimum and maximum values ​​of indoor temperature, and formula (27) is the power limit of TCLs; formula (28) is the balance constraint of the BESS energy storage state; formula (29) is the minimum and maximum value constraints of the BESS energy storage state; formula (30) is the minimum and maximum value constraints of the BESS charging and discharging power; and formula (31) is the balance constraint of the EVs energy storage state. Formula (32) is the minimum and maximum constraints on the EVs' energy storage state; Formula (33) is the minimum and maximum constraints on the EVs' charge and discharge power; Formula (34) is the initial energy storage state when the EVs arrive; Formula (35) indicates that the EVs' energy storage state should be higher than the expected value to support the next trip; Formula (36) indicates that before the EVs arrive, their charge and discharge power should be zero; Formula (37) indicates that the quota sold from the mth prosumer to the nth prosumer is equal to the quota purchased by the nth prosumer from the mth prosumer; Formula (38) indicates that the sum of the initial allocated quota and the purchased quota should be greater than the indirect carbon emissions generated by using utility electricity generated from non-renewable energy sources. This suggests that local energy trading is more encouraged because local energy comes from distributed generators, such as rooftop photovoltaic systems.

[0106] It should be noted that the embodiment of the present invention includes multiple interrelated core models, which form an organic whole around "energy-carbon trading-equipment scheduling". Among them, the distribution network energy scheduling model, as the upper global model, is the top-level framework of the entire system. It is responsible for energy scheduling and carbon emission control at the distribution network level, coordinating the energy flow of the power generation side (micro-generators), the transmission and distribution side (OLTC, capacitor banks, feeders) and the demand side (producers and consumers); the carbon emission flow model adapted to the battery energy storage system is used to connect the energy flow and the carbon flow, quantify the indirect carbon emissions of producers and consumers when using grid power or local energy, and provide a basis for carbon trading and quota allocation; the point-to-point energy and carbon emission rights joint trading model mainly focuses on the energy and carbon quota transactions between producers and consumers on the demand side, integrates the operating characteristics of diversified equipment (BESS, EV, TCL), and forms a micro-trading mechanism for the local energy market (LEM).

[0107] More specifically, the distribution network energy dispatch model provides the node injection power (such as photovoltaic output, thermal power unit power, Formula 4) and equipment operating status (BESS charging and discharging status) to the carbon emission flow model of the adaptive battery energy storage system; the carbon emission flow model of the adaptive battery energy storage system feeds back the node carbon intensity (Formula 18) and BESS dynamic carbon intensity (Formula 19) to the distribution network energy dispatch model, which are used to calculate the carbon emissions of thermal power units (Formula 2) and the indirect carbon emissions of producers and consumers (Formula 38); and the carbon quota purchase constraint of the distribution network (Formula 2) depends on the carbon emissions of thermal power units calculated by the carbon emission flow model of the adaptive battery energy storage system, and the node voltage / current constraints (Formulas 8-9) limit the energy flow path of P2P transactions between producers and consumers (such as priority for transactions between adjacent nodes).

[0108] Furthermore, the distribution network energy dispatch model provides the grid purchase and sale prices and renewable energy power generation (such as photovoltaic output, Formula 4) for point-to-point energy and carbon emission rights joint transactions. The joint transaction model feeds back the net load of producers and consumers (the remaining demand after renewable energy self-sufficiency, Formula 23) and the total carbon trading volume (affecting the global carbon quota balance, Formula 38) to the distribution network. The energy balance constraint of producers and consumers (Formula 23) must meet the power balance of the distribution network (Formulas 4-5), and the feeder capacity constraint (Formula 6) limits the maximum transaction power between producers and consumers (such as the transaction power of adjacent nodes does not exceed the line capacity).

[0109] Furthermore, the point-to-point energy and carbon emission rights joint trading model provides the carbon emission flow model of the adaptive battery energy storage system with the electricity source of the prosumer (grid purchase electricity / local trading electricity) and the BESS charging and discharging power (affecting the carbon intensity update, Formula 19). The carbon emission flow model of the adaptive battery energy storage system outputs the grid power carbon intensity to the point-to-point energy and carbon emission rights joint trading model, which is used to calculate the prosumer's indirect carbon emissions (Formula 38) and carbon trading price. The prosumer's carbon quota balance constraint (Formula 38) requires that "initial quota + purchased quota ≥ carbon emissions generated by grid purchase electricity", which directly relies on the carbon footprint tracking results of the carbon emission flow model. The carbon intensity during EV charging is dynamically calculated by the carbon emission flow model based on the grid carbon intensity during the charging period.

[0110] In an optional embodiment, step S4 specifically includes:

[0111] Decomposing the peer-to-peer energy and carbon emission rights joint trading model into energy trading sub-problems, peer-to-peer trading sub-problems, and carbon emission rights trading sub-problems;

[0112] The fast alternating direction multiplier method is used to iteratively update the variables and Lagrange multipliers in the energy trading subproblem, the peer-to-peer trading subproblem, and the carbon emission rights trading subproblem, narrowing the gap between the objective function and the constraints.

[0113] When the gap is smaller than a set threshold, the iteration is stopped to obtain the final carbon emission rights trading result.

[0114] Specifically, equations (22) to (38) constitute a centralized problem. In a centralized approach, a central processor is required to obtain complete information from prosumers. To protect private information and improve computational efficiency, embodiments of the present invention propose a distributed solution to this problem.

[0115] Specifically, the centralized problem can be decomposed into multiple local subproblems expressed in the enhanced Lagrangian form as follows:

[0116]

[0117] Among them, ρ is the penalty factor, and λ t CAR It is the Lagrange multiplier and also the price of P2P energy trading and carbon emission rights trading.

[0118] Then, the local subproblem is iteratively solved by sequentially updating and exchanging three types of variables (i.e., primary variables, auxiliary variables, and dual variables).

[0119] An auxiliary variable z is introduced to represent information exchange. Then, each prosumer solves the subproblem simultaneously according to the alternating direction method of multipliers (ADMM). The steps are as follows:

[0120] Step 1: Update the primary variable using the following formula:

[0121]

[0122] Step 2: Update the auxiliary variables by the following formula:

[0123]

[0124] Step 3: Update the dual variable using the following formula:

[0125]

[0126] in k is the number of iterations.

[0127] Convergence is determined by the original residual and the dual residual, and its expression is:

[0128]

[0129] Among them, r (k) and s (k)are the primal residual and the dual residual, respectively. The iteration stops when the residuals are within an acceptable accuracy level.

[0130] Because the BESS and the thermostatically controlled load (TCL) have time coupling constraints, the time index t cannot be decoupled when solving the P2P trading problem, and optimizing the decision-making of the entire time domain requires a long computational time. Therefore, in order to efficiently solve the trading problem while considering the future operation of distributed energy resources (DER), the embodiment of the present invention uses the fast ADMM (F-ADMM) algorithm to accelerate the solution process. The F-ADMM algorithm uses a prediction-correction type acceleration step in the dual variable update step. Therefore, using the F-ADMM algorithm, the following formula can be used to update the dual variable (price signal):

[0131]

[0132]

[0133] in, It is an acceleration operator for prosumer i. Its initial value can start from 1 and will gradually increase in each iteration.

[0134] It is worth noting that the fast alternating direction multiplier algorithm can decompose complex problems into sub-problems for distributed solution. In solving the joint trading model of energy and carbon emission rights, it can not only improve computing efficiency, but also protect the privacy of all participating entities, increase transaction decision-making speed, and achieve efficient joint management.

[0135] In summary, the embodiments of the present invention provide a method for the joint management of local energy and carbon emission rights. First, power data from power generation equipment and battery energy storage systems is obtained by solving the distribution network energy dispatch model, providing accurate basic information for subsequent energy management. This information can be used to rationally schedule the operation of power generation and energy storage equipment, improving energy utilization efficiency. Furthermore, using a carbon emission flow model adapted to the battery energy storage system, carbon intensity data for local energy nodes is calculated based on the power data, achieving precise quantification of carbon emissions. This helps accurately understand the carbon emissions of each node and provides a reliable basis for carbon emission rights trading and management. A fast alternating direction multiplier algorithm is then used to solve the point-to-point joint energy and carbon emission rights trading model to obtain the carbon emission rights trading results. This process can incentivize market participants to participate in carbon trading, promote the rational allocation of carbon emission rights, encourage all parties to proactively reduce emissions, and help achieve carbon reduction targets. Finally, the carbon emission rights trading results are fed back into the distribution network energy dispatch model, forming a closed-loop optimization mechanism that adjusts energy dispatch based on carbon trading results, ensuring that energy distribution is more consistent with the requirements of a low-carbon economy, further optimizing energy utilization in the distribution network, and reducing energy costs.

[0136] like Figure 2As shown in the figure, in the distribution system, three micro-generators are located at nodes (Generator) 6, 14, and 25; and BESSs are located at nodes 9, 18, and 33. Assume that there is a local energy system (LES) at node 17, which consists of five prosumers that can trade energy with each other.

[0137] In order to verify the technical feasibility of "scheduling-tracking-trading-feedback" of a local energy and carbon emission rights joint management method provided by the implementation of the present invention, the embodiment of the present invention completed a simulation experiment on a PC equipped with an Intel Core(TM) i7-9750CPU@2.60GHz processor, 16.00GB memory and GTX GeForce 1660Ti graphics card.

[0138] See also Figures 3 to 10 , Figures 3 to 10 The core operating effects and key technology verification of the embodiments of the present invention are intuitively demonstrated through experimental data.

[0139] See also Figure 3 , Figure 3 3 is a schematic diagram of the scheduling results of micro-generators in the distribution network provided by an embodiment of the present invention. Figure 3 The main display is the power (Power) output changes of micro-generators (Generator6, Generator14 and Generator25) within the scheduling period (Time), which can reflect the optimal scheduling of traditional power generation resources by the distribution network energy scheduling model, and reflect the balance between power generation cost and carbon quota cost in the objective function (Formula 1). Figure 3 The scheduling results shown can track the carbon footprint from the power generation side to the demand side.

[0140] See also Figure 4 , Figure 4 Schematic diagram of the carbon intensity of the battery energy storage system provided by an embodiment of the present invention. Figure 4 This example primarily shows the evolution of the carbon intensity of the BESS (9, 18, 33) over time. When the BESS absorbs electricity from renewable sources, its carbon intensity decreases; when it absorbs electricity from non-renewable sources, its carbon intensity increases. This verifies the effectiveness of the improved CEF model (Equation 19) used in this embodiment of the present invention. The BESS carbon intensity is updated only during charging and fluctuates with the cleanliness of the absorbed energy (e.g., carbon intensity decreases at noon when photovoltaic power is abundant).

[0141] See also Figure 5 , Figure 5 This is a schematic diagram of node carbon intensity provided by an embodiment of the present invention. Figure 5The graph shows the evolution of carbon intensity (Carbon Intensity) over time at node 17 in the local energy system (LES). It shows that carbon intensity is relatively low during the midday hours. This indicates that when prosumers in the LES purchase electricity from the utility grid, indirect emissions are lower, while at other times, when consuming electricity from the utility grid, indirect emissions are higher. This indicates that carbon intensity is correlated with the power generation mix, demonstrating the practicality of the carbon footprint tracking model (Equations 20-21) and providing a quantitative basis for demand-side carbon management.

[0142] See also Figure 6 , Figure 6 This is a schematic diagram of the evolution of peer-to-peer transactions provided by an embodiment of the present invention. Figure 6 The figure shows the convergence results of the distributed algorithm, revealing the trading behavior of Prosumer 1 with the other four prosumers (2, 3, 4, and 5) at 12:00. For example, P12 represents the fluctuation in power from Prosumer 1 to Prosumer 2 at different iterations, and P21 represents the fluctuation in power from Prosumer 2 to Prosumer 1 at different iterations. It can be seen that the trading decision ultimately converged within 150 iterations, and the amount of energy sold by Prosumer 1 to the other prosumers was equal to the amount of energy received by the other prosumers from Prosumer 1. This primarily verifies the convergence of the F-ADMM algorithm and the feasibility of distributed trading.

[0143] See also Figure 7 , Figure 7 Schematic diagram of the scheduling results of the battery energy storage system provided by an embodiment of the present invention. Figure 7 This chart primarily displays the charging and discharging power and energy storage status of the BESS owned by prosumer 1. Charging activity is concentrated during midday, while discharging activity is concentrated during the evening peak period. This is because midday offers abundant solar energy, while evening peak demand is high and solar energy is insufficient. This primarily reflects the practical application of the BESS constraints (Equations 28-30).

[0144] See also Figure 8 , Figure 8 This is a schematic diagram of the scheduling result of the constant temperature control load provided by the embodiment of the present invention. Figure 7 The results of the thermostatic load operation show that the indoor temperature can be maintained below 27°C. Because the outdoor temperature is higher at noon, the thermostatic load power is higher during this period. This primarily verifies the accuracy of the thermodynamic model (Equations 25-27).

[0145] See also Figure 9 , Figure 9 Schematic diagram of the dispatching results of electric vehicles provided by an embodiment of the present invention. Figure 9It mainly shows the change of EV charging and discharging power and energy status over time. Figure 9 As can be seen, the EV is not at home between 6:00 AM and 3:00 PM, so there is no charging or discharging. When the EV returns home at 3:00 PM, it first discharges energy through vehicle-to-grid (V2G) and vehicle-to-home (V2H) interactions. After 10:00 PM, the EV begins charging during the off-peak period.

[0146] See also Figure 10 , Figure 10 This is a schematic diagram of the carbon emission rights trading results of electric vehicles provided by an embodiment of the present invention. Figure 10 It mainly shows the carbon emission rights trading behavior (selling quotas) of prosumer 1. According to the joint trading model proposed in the embodiment of the present invention, prosumer 1 will sell carbon emission rights to other prosumers, reflecting the constraint validity of formula 38 (initial quota + purchase quota ≥ indirect emissions), which encourages clean energy consumption. That is, the prosumer mainly consumes clean energy and therefore has remaining quotas that can be sold.

[0147] In order to further verify the superiority of the local energy and carbon emission rights joint management method provided by the implementation of the present invention, the following will describe the specific circumstances of verifying the method in the modified IEEE 30-node system through three scenarios.

[0148] Specifically, the following three scenarios are set:

[0149] Scenario 1: Establishing a local energy market LEM, where both energy and carbon emission rights can be traded (method provided in an embodiment of the present invention).

[0150] Scenario 2: Local energy trading is not considered, and end users can only buy energy from or sell energy to the grid.

[0151] Scenario 3: LEM is established, but the double taxation mechanism is not applied, and there is no carbon emission rights trading between producers and consumers.

[0152] See also Figures 11-13 , shows the energy purchase and sales behavior of prosumer 1 under these three scenarios. Positive values ​​indicate purchase behavior, and negative values ​​indicate sales behavior. Figures 11-13 , it can be seen that the total purchase or sale profiles of the three scenarios are almost identical, but the components differ. For example, in Scenario 2, there is no local trading, so all energy comes from or flows to the grid. In Scenario 1 and Scenario 3, some energy comes from or flows to the grid, and some energy is traded within the LEM. Comparing Scenario 1 and Scenario 3, it can be seen that in Scenario 1, the amount of energy traded within the LEM is greater.

[0153] More specifically, see Table 2, which quantitatively compares the three scenarios in terms of average daily costs, total LEM energy imports, total LEM energy exports, self-sufficiency ratio, and total indirect emissions for the entire LES. The self-sufficiency ratio is defined as the ratio of total energy traded in the LEM to total energy traded.

[0154] Table 2 Comparison of transactions in different scenarios

[0155] Scenario 1 Scenario 2 Scenario 3 Average daily cost $21.21 $26.23 $22.21 Total energy received from LEM 22.89kWh 0kWh 18.27kWh Energy delivered to LEM 23.43kWh 0kWh 13.14kWh Self-sufficiency rate 58.23％ 0％ 32.71％ Total indirect emissions 5.92kg 13.34kg 9.63kg

[0156] Table 2 shows that LEM imports and exports are greater in Scenario 1. The self-sufficiency rate in Scenario 1 is 58.23%, 26% higher than in Scenario 3, indicating that prosumers are more willing to participate in LEM trading. Although the introduction of a carbon tax in Scenario 1 increases costs, the average daily cost is still slightly lower than in Scenario 3 because prosumers benefit from localized trading. In terms of environmental benefits, Scenario 1 generates significantly lower indirect emissions than the other scenarios.

[0157] See also Figure 14 , Figure 14 This is a structural block diagram of a local energy and carbon emission rights joint management system 200 provided by an embodiment of the present invention. The local energy and carbon emission rights joint management system 200 includes:

[0158] The distribution network energy dispatch module 21 is used to solve the distribution network energy dispatch model and obtain the power data of the power generation equipment and the battery energy storage system;

[0159] a carbon emission tracking module 22 for calculating carbon intensity data of local energy nodes based on the power data by adapting a carbon emission flow model of the battery energy storage system;

[0160] The carbon emission rights trading module 23 is configured to solve the point-to-point energy and carbon emission rights joint trading model based on the node carbon intensity data using a fast alternating direction multiplier algorithm to obtain a carbon emission rights trading result;

[0161] The distribution network energy optimization and scheduling module 24 is used to feed back the carbon emission rights trading results to the distribution network energy scheduling model to perform energy optimization and scheduling on the distribution network.

[0162] It should be noted that the local energy and carbon emission rights joint management system provided in an embodiment of the present invention is used to execute all the process steps of a local energy and carbon emission rights joint management method of the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0163] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for joint management of local energy and carbon emission rights, characterized in that: include: Solve the distribution network energy dispatch model to obtain power data of power generation equipment and battery energy storage systems; Based on the power data, calculate the carbon intensity data of the local energy node by adapting the carbon emission flow model of the battery energy storage system; Based on the node carbon intensity data, a fast alternating direction multiplier algorithm is used to solve the point-to-point energy and carbon emission rights joint trading model to obtain the carbon emission rights trading results; The carbon emission rights trading results are fed back to the distribution network energy scheduling model to perform energy optimization scheduling on the distribution network.

2. The local energy and carbon emission rights joint management method according to claim 1, characterized in that: The distribution network energy dispatch model aims to minimize the comprehensive cost including energy purchase, power generation, carbon emission quota purchase, equipment degradation, load reduction and energy loss; the constraints of the distribution network energy dispatch model include carbon quota purchase constraints, thermal power unit output constraints, power flow balance constraints, feeder capacity constraints, voltage amplitude constraints, current amplitude constraints, on-load tap-changing transformer state constraints, capacitor bank operating state constraints, battery energy storage system storage state balance constraints, and photovoltaic inverter power output constraints.

3. The local energy and carbon emission rights joint management method according to claim 1, characterized in that: The point-to-point energy and carbon emission rights joint trading model aims to minimize the comprehensive costs including the income from purchasing and selling electricity, point-to-point transactions and the environment on the demand side; the constraints of the point-to-point energy and carbon emission rights joint trading model include energy balance constraints, constant temperature control load power constraints, battery energy storage system energy storage state balance constraints, battery energy storage system charging and discharging power constraints and electric vehicle charging and discharging power constraints.

4. The method for joint management of local energy and carbon emission rights according to claim 2, characterized in that: The objective function of the distribution network energy dispatch model is: Where t represents time, i represents the i-th grid node in the distribution network, and Ω ST represents a collection of substations, represents the wholesale market electricity price at time t, represents the power output of the thermal power unit at the grid node i at time t, Δt represents the time interval, Ω G represents the set of thermal power units, a i 、b i and c i represents the fuel cost coefficient of the thermal power unit in the grid node i, represents the carbon price at time t, represents the number of carbon quotas purchased by the thermal power unit at grid node i at time t, Ω BESS represents the set of battery energy storage systems, τ is the cost coefficient of battery life degradation, represents the discharge power of the battery energy storage system at grid node i at time t, Indicates the leakage loss coefficient of the battery, represents the energy storage state of the battery energy storage system at grid node i at time t, χ represents the value of the reliability cost, represents the load reduction of grid node i at time t, j represents other grid nodes connected to transmission grid node i, Ω B represents the set of feeders, I ij,t represents the square value of the current at time t on the line between grid nodes i and j, r ij Represents the resistance value of the feeder between grid node i and node j.

5. The method for joint management of local energy and carbon emission rights according to claim 4, characterized in that: The constraints of the distribution network energy dispatch model include: (V i min ) 2 ≤v i,t ≤(V i max ) 2 ; in, represents the carbon quota initially allocated to the thermal power unit at the grid node i, represents the carbon quota purchased at grid node i, represents the carbon intensity of thermal power units at grid node i at time t, P i G,min represents the minimum power output of the thermal power unit at the grid node i, P i G,max represents the maximum power output of the thermal power unit at grid node i, k represents the grid nodes other than grid nodes i and j, Ω represents the set of distribution network nodes, represents the active power flow at time t on the line between grid node j and node k, represents the active power flow at time t on the line between grid node i and node j, represents the output of the photovoltaic system at grid node j at time t, represents the discharge power of the battery energy storage system at grid node j at time t, represents the charging power of the battery energy storage system at grid node j at time t, represents the active load of grid node j at time t, represents the active load reduction of grid node j at time t, represents the reactive power flow at time t on the line between grid node j and node k, represents the reactive power flow at time t on the line between grid node i and node j, x ij represents the reactance of the feeder between grid nodes i and j, represents the reactive power output of the capacitor bank at grid node j at time t, represents the output power of the photovoltaic inverter at grid node j at time t, represents the reactive load of grid node j at time t, represents the reactive load reduction of grid node j at time t, represents the capacity of the feeder between grid nodes i and j, v j,t represents the square value of the voltage of grid node j at time t, v i,t Represents the square value of the voltage of grid node i at time t, V i min Indicates the minimum square voltage value of grid node i, V i max represents the maximum square value of the voltage at grid node i, represents the minimum square value of the current on the line between grid node i and node j, represents the maximum square value of the current on the line between grid node i and node j, v 1,t represents the reference node voltage at time t, It represents the square value of the primary side voltage of the on-load tap-changing transformer, r min Indicates the minimum resistance value on the grid node line, tap t Indicates the state of the on-load tap-changing transformer at time t, ΔV T is the single-step voltage adjustment value, tap t+1 Indicates the state of the on-load tap-changing transformer at time t+1, tap max Indicates the maximum adjustable state of the on-load tap-changing transformer, c i,x,t represents the state of capacitor bank x at grid node i at time t, c i,x,t+1 represents the state of capacitor bank x at grid node i at time t+1, represents the maximum capacity of the capacitor bank x on the grid node i, represents the reactive power provided by the capacitor bank at grid node i at time t, It represents the capacitor bank set on the grid node i, q i,x,t represents the reactive power output of capacitor bank x at grid node i at time t, represents the charging power of the battery energy storage system at grid node i at time t, P i CH,max represents the maximum charging power of the battery energy storage system at grid node i at time t, represents the discharge power of the battery energy storage system at grid node i at time t, P i DS,max represents the maximum discharge power of the battery energy storage system at grid node i at time t, represents the energy storage state of the battery energy storage system at grid node i at time t+1, η CH represents the charging efficiency of the battery energy storage system, η DS Indicates the charging efficiency of the battery energy storage system, represents the minimum value of the battery energy storage system energy at grid node i, represents the maximum energy of the battery energy storage system at grid node i, represents the output power of the photovoltaic inverter at grid node i at time t, represents the photovoltaic system capacity of grid node i, represents the output of the photovoltaic system at grid node i at time t.

6. The local energy and carbon emission rights joint management method according to claim 1, characterized in that: The carbon emission flow model adapted to the battery energy storage system considers the charging, discharging, and idle states of the battery energy storage system and quantifies the indirect carbon emissions generated by prosumers by calculating the carbon intensity of the nodes. The carbon emission flow model of the adapted battery energy storage system is: in, represents the node strength of grid node i at time t, represents the power output of the thermal power unit at grid node i at time t, represents the node carbon intensity of thermal power unit at grid node i, represents the discharge power of the battery energy storage system at grid node i at time t, represents the energy storage carbon potential of grid node i at time t, represents the set of transmission lines transmitted to grid node i, b represents the b-th branch transmission line, represents the power flow of branch b at time t, represents the branch strength of the b-th branch at time t, represents the output of the photovoltaic system at grid node i at time t.

7. The method for joint management of local energy and carbon emission rights according to claim 3, characterized in that: The objective function of the peer-to-peer energy and carbon emission rights joint trading model is: Where t represents time, Δt represents the time interval, m and n represent producers and consumers, Ω P Represents the set of P2P transaction producers and consumers, represents the set of P2P transaction producers and consumers except producer and consumer m, represents the price at which the prosumer purchases electricity from the grid at time t, represents the electricity purchased by prosumer m from the grid at time t, represents the price at which the prosumer sells electricity to the grid at time t, represents the electricity sold by prosumer m to the grid at time t, d mn The electrical distance between prosumer m and prosumer n, represents the P2P transaction power between prosumer m and prosumer n, represents the carbon price at time t, Represents the carbon emission rights trading volume between prosumer m and prosumer n.

8. The method for joint management of local energy and carbon emission rights according to claim 7, characterized in that: The constraints of the peer-to-peer energy and carbon emission rights joint trading model include: When t≤T arrive hour; in, represents the charging power of the electric vehicle of prosumer m at time t, represents the electricity sold by prosumer m to the grid at time t, represents the P2P transaction power between prosumer m and prosumer n, represents the battery energy storage system charging power of prosumer m at time t, represents the discharge power of the battery energy storage system of prosumer m at time t, represents the power of the thermostatically controlled load of the prosumer m at time t, represents the charging power of the electric vehicle of prosumer m at time t, represents the electric vehicle discharge power of prosumer m at time t, represents the load of prosumer m at time t, represents the renewable energy generation of prosumer m at time t; represents the indoor temperature of prosumer m at time t, represents the indoor temperature of prosumer m at time t+1, Represents the thermal work parameter, R m represents the thermal resistance of the prosumer m, T t out represents the outdoor temperature at time t, represents the minimum value of the outdoor temperature of the prosumer m, represents the maximum outdoor temperature of prosumer m, represents the minimum power of the thermostatically controlled load of prosumer m, represents the maximum power of the thermostatically controlled load of prosumer m; represents the energy storage state of the battery energy storage system of prosumer m at time t+1, represents the energy storage state of the battery energy storage system of prosumer m at time t, represents the charging power of the battery energy storage of prosumer m, represents the discharge power of the battery energy storage of prosumer m, represents the minimum energy value of the battery energy storage system of prosumer m, represents the maximum energy of the battery energy storage system of prosumer m, represents the maximum value of the battery charge and discharge power of prosumer m, represents the maximum value of the battery charge and discharge power of prosumer m, represents the battery charging power or discharging power of prosumer m at time t; represents the energy storage state of the electric vehicle of prosumer m at time t, represents the energy storage state of the electric vehicle of prosumer m at time t+1, represents the charging power of the electric vehicle of prosumer m at time t, represents the electric vehicle discharge power of prosumer m at time t, represents the minimum energy storage state of the electric vehicle of prosumer m, represents the maximum energy storage state of the electric vehicle of prosumer m, represents the minimum charging and discharging power of the electric vehicle of prosumer m, represents the maximum charge and discharge power of the electric vehicle of prosumer m, T arrive represents the arrival time of the electric vehicle, The electric vehicle of prosumer m arrives at time T arrive The energy storage state, represents the initial energy storage state of the electric vehicle of prosumer m upon arrival, represents the expected energy storage state of the electric vehicle of prosumer m when it leaves; represents the carbon emission rights trading volume at time t when prosumer m sells carbon emission rights to prosumer n, represents the carbon emission rights trading volume at time t when prosumer n sells carbon emission rights to prosumer m, represents the initial allocation of carbon quotas to the thermal power units of prosumer m, represents the electricity purchased by prosumer m from the grid at time t, represents the node strength at time t.

9. The method for joint management of local energy and carbon emission rights according to claim 1, characterized in that: Based on the node carbon intensity data, a fast alternating direction multiplier algorithm is used to solve the point-to-point energy and carbon emission rights joint trading model to obtain the carbon emission rights trading results, including: Decomposing the peer-to-peer energy and carbon emission rights joint trading model into energy trading sub-problems, peer-to-peer trading sub-problems, and carbon emission rights trading sub-problems; The fast alternating direction multiplier method is used to iteratively update the variables and Lagrange multipliers in the energy trading subproblem, the peer-to-peer trading subproblem, and the carbon emission rights trading subproblem, narrowing the gap between the objective function and the constraints. When the gap is smaller than a set threshold, the iteration is stopped to obtain the final carbon emission rights trading result.

10. A local energy and carbon emission rights joint management system, characterized in that: include: The distribution network energy dispatch module is used to solve the distribution network energy dispatch model and obtain the power data of the power generation equipment and battery energy storage system; a carbon emission tracking module, configured to calculate carbon intensity data of local energy nodes based on the power data by adapting a carbon emission flow model of the battery energy storage system; A carbon emission rights trading module is used to solve a point-to-point energy and carbon emission rights joint trading model based on the node carbon intensity data using a fast alternating direction multiplier algorithm to obtain a carbon emission rights trading result; The distribution network energy optimization scheduling module is used to feed back the carbon emission rights trading results to the distribution network energy scheduling model to perform energy optimization scheduling on the distribution network.

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