Source storage multi-target cooperative control method for residential users

Abstract

The invention discloses a resident user-oriented source storage multi-target cooperative control method. The method comprises the following steps of: 1, acquiring a load operation state of an electrical load, technical parameters of photovoltaic equipment, meteorological data and technical parameters of energy storage equipment; 2, constructing an electrical load and source storage equipment operation characteristic model; 3, constructing a source storage multi-target cooperative control model; and 4, solving the electrical load and source storage equipment operation characteristic model in the step 2 and the source storage multi-target cooperative control model in the step 3 by using a chaotic particle swarm algorithm as an optimization solution algorithm to obtain an optimal solution of source storage multi-target cooperative control. According to the resident user-oriented source storage multi-target cooperative control method, for three types of regulation and control subjects of resident household load, photovoltaic power generation and energy storage, operation characteristics of electrical load and source storage equipment are analyzed, and a multi-target source storage cooperative control model is constructed; and according to an optimization algorithm, obtaining a scheduling mode of daily minimum power consumption cost, optimal balance power and maximum carbon emission reduction.

Description

Technical Field

[0001] The present invention relates to a source-storage multi-objective collaborative control method for residential users, and belongs to the technical field of source-grid-load-storage collaborative control for power users. Background Art

[0002] Under the new energy pattern, the power supply structure, power consumption structure and system ecology in the new power system will undergo profound changes. Residential household appliances, household photovoltaics and energy storage loads have become important grid-load interactive resources. However, in terms of the operation and maintenance costs of source and storage equipment, power self-balancing and carbon emission reduction effects, there is a lack of accurate quantitative data and multi-objective optimal coordinated scheduling methods, which makes it impossible to fully perceive the economy, stability and low carbon nature of the coordinated operation of source, grid, load and storage. It is urgent to fully tap the adjustable potential of residential users' source and storage equipment through technical means, promote coordinated scheduling between equipment, and achieve cost minimization, power balance optimization, and carbon emission reduction maximization.

[0003] Therefore, a multi-objective collaborative control method of sources and storage for residential users is needed to solve the above problems. Summary of the invention

[0004] Purpose of the invention: In view of the problems existing in the prior art, the present invention provides a multi-objective collaborative control method of sources and storage for residential users.

[0005] A multi-objective coordinated control method for source and storage of residential users includes the following steps:

[0006] Step 1: Collect the load operation status, photovoltaic equipment technical parameters, meteorological data and energy storage equipment technical parameters of the power load in the period t, wherein the power load includes rigid load, shiftable load and reducible load;

[0007] Step 2: Constructing the power load and source storage equipment operation characteristic model: According to the load operation status of step 1, the power load operation characteristic model is obtained; according to the photovoltaic equipment technical parameters, meteorological data and energy storage equipment technical parameters of step 1, the source storage equipment operation characteristic model is obtained;

[0008] Step 3: Construct a source-reservoir multi-objective collaborative control model: The source-reservoir multi-objective collaborative control model includes a source-reservoir collaborative control optimization strategy and a source-reservoir collaborative operation constraint strategy.

[0009] The source-storage collaborative control optimization strategy includes minimizing daily electricity costs, optimizing average hourly power stability, and maximizing daily carbon emission reduction;

[0010] The source-storage coordinated operation constraint strategy includes the residential load boundary, the photovoltaic reverse power boundary and the energy storage operation boundary;

[0011] Step 4: Use the chaotic particle swarm algorithm as the optimization algorithm to solve the power load and source storage equipment operating characteristic model of step 2 and the source storage multi-objective collaborative control model of step 3 to obtain the optimal solution for the source storage multi-objective collaborative control.

[0012] Furthermore, the power load operation characteristic model in step 2 includes a rigid load curve, a shiftable load curve and a curtailable load curve, and the source storage equipment operation characteristic model in step 2 includes a photovoltaic power generation curve and an energy storage operation characteristic curve.

[0013] Furthermore, the rigid load curve is expressed by the following formula:

[0014] P ri' (t) = P ri (t)·X ri (t)

[0015] Where P ri' (t) is the actual rigid load power during period t, P ri (t) is the rigid load power in a given period of time t, X ri (t) is the actual working state of the rigid load;

[0016] The translatable load curve is represented by the following formula:

[0017] P tr' (t) = P tr (t)·X tr (t)

[0018] Where P tr' (t) is the actual load power that can be transferred during period t, P tr (t) is the load power that can be shifted in a given period of time, X tr (t) is the actual working state of the load that can be translated during period t;

[0019] The curtailable load curve is expressed by the following formula:

[0020] P cut' (t) = P cut (t)·(1-X cut (t))

[0021] Where P cut' (t) is the load power that can be actually reduced during period t, P cut (t) is the load power that can be reduced in a given period of time, X cut (t) is the reducibility factor;

[0022] The photovoltaic power generation curve is expressed by the following formula:

[0023]

[0024] P pv-out (t) = η inv ·P PV (t)

[0025] Where P pv (t) is the photovoltaic power generation power in period t, P pv-out (t) is the photovoltaic output power during period t, P stc is the maximum output power of the photovoltaic cell under given standard conditions, K stc is the light intensity, T stc is temperature, η C is the conversion efficiency of the solar panel, K C (t) is the solar illumination intensity during period t, T(t) is the temperature of the environment where the photovoltaic cell is located, η inv is the photovoltaic inverter efficiency;

[0026] The energy storage operation characteristic curve is expressed by the following formula:

[0027] SOC(t+1)=Q(t+1) / Q ed

[0028]

[0029] P L (t) = P ri' (t)+P tr' (t)+P cut' (t)

[0030]

[0031]

[0032] In the formula, SQC(t+1) is the state of charge of the energy storage battery at time t+1, Q(t) is the power of a given energy storage battery at time t, Q(t+1) is the power of a given energy storage battery at time t+1, and Q ed is the rated capacity of the energy storage battery, P ch (t) is the charging power of the energy storage battery during period t, P disc (t) is the discharge power of the energy storage battery during period t, P pv-out (t) is the photovoltaic output power in a given period of time, P L (t) is the load power for a given period of time, P ch-max is the maximum charging power of the energy storage battery, P disc-max is the maximum discharge power of the energy storage battery.

[0033] Furthermore, the objective function minC in step 3 with the minimum daily electricity cost is total It is expressed by the following formula:

[0034] minC total =C buy -C cell +C bat +C pv

[0035]

[0036]

[0037]

[0038]

[0039] Where T is the given scheduling period, c buy (t) is the electricity purchase cost of the upper power grid during period t, c cell (t) is the electricity sales fee of the upper power grid during period t, P buy (t) is the power purchased by the upper power grid during period t, P cell (t) is the power sold by the upper power grid during period t, K bat K is the energy storage battery operation cost coefficient, pv is the photovoltaic operation cost coefficient, Δt is the time period, C buy and C cell is the electricity purchase and sales costs from the upper grid in a given dispatch period, C bat is the cost of energy storage in a given dispatch cycle, C pv is the cost input of PV in a given dispatch period;

[0040] The objective function for optimizing the average hourly power stability in step 3 is expressed as follows:

[0041]

[0042] P load (t) = P ri' (t)+P tr' (t)+P cut' (t)+P ch (t)

[0043] P supply (t) = P pv-out (t)+P disc (t)+P grid (t)

[0044] Where P load (t) is the total load of residential users in period t, P supply (t) is the total power supply power in period t, P ri'(t) is the actual rigid load power during period t, P tr' (t) is the actual load power that can be transferred during period t, P cut' (t) is the load power that can be actually reduced during period t, P ch (t) is the charging power of the energy storage battery during period t, P pv-out (t) is the photovoltaic output power in a given period of time, P disc (t) is the discharge power of the energy storage battery during period t, P grid (t) is the power supply power given by the power grid company, and T is the given dispatching period;

[0045] The objective function maxER for maximizing daily carbon emission reduction in step 3 is expressed as follows:

[0046]

[0047] Where T is the given scheduling period, P pv (t) is the photovoltaic power generation power in period t, Δt is the time period, EF grid,t is the real-time dynamic carbon factor obtained from the carbon meter, P buy (t) is the purchased power at time t obtained by the smart meter and the electricity consumption information collection system, P EB-buy (t) is the historical baseline purchased power at the same time obtained by the smart meter and the electricity consumption information collection system.

[0048] Furthermore, the residential load boundary in step 3 is expressed as follows:

[0049]

[0050]

[0051] t tr-end -t tr-str +1 = T tr

[0052] 0≤X cut (t)≤1

[0053] Where, t ri-str and t ri-end are the starting and ending working time of a given rigid load, t tr-str and t tr-end are the start and end working time of the movable load, T tr is the working time of the shiftable load, and the load factor range that can be reduced is [0,1], X ri (t) is the rigid load working state, X tr (t) is the working state of the translatable load, X cut (t) is the reducible load factor;

[0054] The photovoltaic reverse power boundary is expressed by the following formula:

[0055] P pv-out (t)-P load (t)≤P grid-max

[0056] Where P grid-max is the maximum PV reverse power specified by a given grid, P pv-out (t) is the photovoltaic output power during period t, P load (t) is the total load of residential users during period t;

[0057] The energy storage operation boundary is expressed by the following formula:

[0058] 0≤P ch (t)≤P ch-max

[0059] 0≤P disc (t)≤P disc-max

[0060] SOC min ≤SOC(t+1)≤SOC max

[0061] Where P ch-max is the maximum charging power of a given energy storage battery, P disc-max is the maximum discharge power of a given energy storage battery, SOC min The minimum state of charge of the energy storage battery, SOC max is the maximum state of charge value of the energy storage battery, P ch (t) is the charging power of the energy storage battery, P disc (t) is the discharge power of the energy storage battery, and SOC(t+1) is the state of charge of the energy storage battery.

[0062] Furthermore, in step 4, the chaotic particle swarm algorithm is used as an optimization algorithm to solve the power load and source storage equipment operation characteristic model in step 2 and the source storage multi-objective collaborative control model in step 3, including the following steps:

[0063] Step 41: Generate chaotic particles x using Logistic image n , determine the maximum number of iterations, the number of particles, and initialize the position and velocity of the particles;

[0064] Step 42: Calculate the fitness value of each particle: For each particle, calculate its fitness value according to the objective function, and record the initial position of the particle as p best , the fitness value of each particle at the current position and p bestIf the fitness value of the current position is better, the particle's p is updated. best is the current location;

[0065] Step 43: Traverse the p of all particles best , find the particle with the best fitness value and store its position in g best At the same time, the value is stored in the global optimal value fg best middle,;

[0066] Step 44: Update the particle's position and velocity using the following formula:

[0067] v id (i+1)=v id (i)+c1·r1·(p best (i)-x id (i))+c2·r2·(g best (i)-x id (i))

[0068] x id (i+1)=x id (i)+v id (i+1)

[0069] In the formula, v id (i) and x id (i) are the velocity and current position of the particle at the i-th iteration, r1 and r2 are random numbers on [0,1], c1 and c2 are learning factors, and p best (i) represents the individual optimal position of the particle in the i-th iteration, g best (i) represents the position of the particle with the best fitness in the i-th iteration group, and i represents the number of iterations;

[0070] Step 45: For the optimal position g best =(g best (1),g best (2),...,g best (D)) Perform chaos optimization and set g best (i), i = 1, 2, 3, ..., D is mapped to the interval [0, 1], where D represents the number of decision variables;

[0071] The chaotic variable sequence is obtained using the following formula:

[0072] x n+1 =μ·x n ·(1-x n ),n=1,2,...,N

[0073] In the formula, μ is the chaos parameter, N is the number of particles;

[0074] By mapping back to the original solution space, the fitness value of each feasible solution is calculated to obtain the optimal feasible solution g * best , replace the position of particles in the current group;

[0075] Step 46: Determine whether the number of iterations has reached the maximum value, and if so, output the optimal solution fg best , otherwise repeat steps 42 to 46.

[0076] Beneficial effect: The multi-objective collaborative control method of sources and storage for residential users of the present invention analyzes the operating characteristics of power loads and source and storage equipment for three types of control subjects: household loads, photovoltaic power generation, and energy storage, and constructs a multi-objective collaborative control model of sources and storage. According to the optimization algorithm, the scheduling method of minimum daily electricity cost, optimal balanced power, and maximum carbon emission reduction is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of the multi-objective collaborative control method of source storage for residential users of the present invention. DETAILED DESCRIPTION

[0078] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings to more clearly and completely illustrate the technical solutions of the present invention.

[0079] The modeling process is as follows:

[0080] 1. Build the source storage equipment operation characteristic model:

[0081] 1) Obtain rigid load power P based on household surveys and smart meter data ri (t), actual working status X ri (t) takes the value of 0 or 1, representing shutdown and startup respectively, and the rigid load power P in period t is calculated. ri (t).

[0082] P ri' (t) = P ri (t)·X ri (t) (1)

[0083] 2) Based on household surveys and smart meter data, obtain the transferable load power P tr (t), start and end working time allowed for the movable load t tr-str ,t tr-end and working hours T tr , actual working status X tr (t) takes the value of 0 or 1, representing shutdown and startup respectively, and the load power P that can be shifted during the t period is calculated. ri (t).

[0084] P tr' (t) = P tr (t)·X tr (t) (2)

[0085] 3) Obtain the load power P that can be reduced based on household surveys and smart meter data cut (t), can reduce X cut (t) has a value range of [0,1], representing the energy saving rate. The load power P that can be reduced in period t is calculated. ri (t).

[0086] P cut' (t) = P cut (t)·(1-X cut (t)) (3)

[0087] 4) According to the technical parameters of photovoltaic panels, obtain the light intensity K under standard conditions stc 1000W / m 2 , temperature T stc The maximum output power P of the photovoltaic cell is 25℃ stc , and the solar panel conversion efficiency η C ;According to the local meteorological data, obtain the solar illumination intensity K during period t C (t), the temperature T(t) of the environment where the photovoltaic cell is located; the photovoltaic inverter efficiency η is obtained according to the technical parameters of the photovoltaic inverter inv , construct the photovoltaic power generation power P during period t pv (t) and photovoltaic output power P pv-out (t).

[0088]

[0089] P pv-out (t) = η inv ·P PV (t) (5)

[0090] 5) According to the photovoltaic output power P during period t pv-out (t), load power P L (t), calculate the charging power P of the energy storage battery during period t ch (t) and discharge power P disc (t); According to the energy storage equipment technology and battery management system, obtain the rated power Q of the energy storage battery ed , the charge Q(t) at time t, and calculate the state of charge SQC(t+1) of the energy storage battery at time t+1.

[0091]

[0092]

[0093] in:

[0094] P L (t) = P ri' (t)+P tr' (t)+P cut' (t)+P EV' (t) (8)

[0095] SOC(t+1)=Q(t+1) / Q ed (9)

[0096]

[0097] 2. Construct a multi-objective source-reservoir collaborative control model:

[0098] 1) Given a dispatching cycle T of 24 hours, the power grid company specifies the electricity purchase cost c for each period buy (t), electricity sales cost c cell (t); The purchased power P of each period is obtained by the smart meter buy (t), power sales power P cell (t); Energy storage battery operation cost coefficient K bat and photovoltaic operation cost coefficient K pv Usually 0.5% to 2% is used to calculate the daily electricity purchase and sales cost C from the upper power grid. grid 、Cost of energy storage bat , Photovoltaic cost investment C pv , we get the objective function expression of minimizing electricity cost minC total .

[0099] minC total =C buy -C cell +C bat +C pv (11)

[0100] in:

[0101]

[0102]

[0103]

[0104]

[0105] 2) The power supply power P is given by the power grid company grid (t), by calculating the total load P of residential users in period t load (t), total power supply Psupply (t), calculate and obtain the objective function expression minC to minimize the electricity cost total .

[0106]

[0107] in:

[0108] P load (t) = P ri' (t)+P tr' (t)+P cut' (t)+P ch (t) (17)

[0109] P supply (t) = P pv (t)+P disc (t)+P grid (t) (18)

[0110] 3) Obtain real-time dynamic electric carbon factor EF from the electric carbon meter grid,t , the purchased power P at time t is obtained by the smart meter and the electricity consumption information collection system buy (t) and the historical baseline power purchase power P at the same time EB-buy (t), the carbon emission reduction of photovoltaic power generation and the carbon emission reduction of electricity purchase are calculated, and the objective function expression of maximizing carbon emission reduction is obtained.

[0111]

[0112] 3. Construct source-storage collaborative operation constraint strategy:

[0113] 1) Based on household surveys and smart meter data, obtain the start and end working time t of the rigid load ri-str ,t ri-end , start and end working time of the movable load t tr-str ,t tr-end and working hours T tr , the load factor range can be reduced to [0,1], and the rigid load working state X ri (t), working state of translatable load X tr (t), load factor that can be reduced X cut (t) Constraints.

[0114]

[0115]

[0116] t tr-end -t tr-str +1 = T tr (twenty two)

[0117] 0≤X cut (t)≤1 (23)

[0118] 2) The maximum photovoltaic reverse power P specified by the power grid grid-max , and the photovoltaic reverse power constraint is obtained.

[0119] P pv-out (t)-P load (t)≤P grid-max (twenty four)

[0120] 3) According to the energy storage equipment technology and battery management system, obtain the maximum charging power P of the battery ch-max , Maximum discharge power P disc-max , minimum battery state of charge SOC min , maximum state of charge value SOC max , get the battery charging power P ch (t), discharge power P disc (t), battery state of charge SOC(t+1) constraint.

[0121] 0≤P ch (t)≤P ch-max (25)

[0122] 0≤P disc (t)≤P disc-max (26)

[0123] SOC min ≤SOC(t+1)≤SOC max (27)

[0124] 4. Construct the optimization solution algorithm of source-reservoir collaborative control model:

[0125] 1) Select the chaotic particle swarm algorithm as the optimization solution algorithm, and use the Logistic image to generate the chaotic variable x n , where the chaos parameter μ is 4, the number of particles N is 40, the maximum number of iterations k is 300, 0≤x0≤1, and the decision variables are the working state of the rigid load, the working state of the shiftable load, the reducible load factor, photovoltaic power generation, and energy storage charging and discharging at each moment.

[0126] x n+1 =f(μ,x n )=4x n ·(1-x n ),n=1,2,...,N (28)

[0127] 2) Calculate the fitness value of each particle. For each particle, calculate its corresponding objective function value and record the current position of the particle as pbest , the particle position with the best fitness in the group is recorded as g best , the global optimal value is recorded as fg best .

[0128] 3) Update the particle's position and velocity. id (i), x id (i) are the velocity and current position of the particle at the kth iteration, r1 and r2 are random numbers on [0,1], and c1 and c2 are both 2.

[0129] v id (i+1)=v id (i)+c1·r1·(pbest(i)-x id (i))+c2·r2·(gbest(i)-x id (i)) (29)

[0130] x id (i+1)=x id (i)+v id (i+1) (30)

[0131] 4) For the optimal position g best =(g best1 ,g best2 ,...,g bestD ) to perform chaos optimization and set g besti ,i=1,2,3,...,D is mapped to the interval [0,1], where D represents the number of decision variables. The chaotic variable sequence is obtained by equation (28), which is returned to the original solution space by mapping. The fitness value of each feasible solution is calculated to obtain the optimal feasible solution g * best , replacing the position of particles in the current group.

[0132] 5) Determine whether the number of iterations has been completed. If it is satisfied, output the optimal solution fg of the global multi-objective optimization problem best , that is, the working status of each load, photovoltaic output, and energy storage charging and discharging status; otherwise, return to step 2) and continue searching.

Claims

1. A multi-objective collaborative control method for source and storage for residential users, characterized in that: The steps include: Step 1: Collect the load operation status, photovoltaic equipment technical parameters, meteorological data and energy storage equipment technical parameters of the power load in the period t, wherein the power load includes rigid load, shiftable load and reducible load; Step 2: Constructing the power load and source storage equipment operation characteristic model: According to the load operation status of step 1, the power load operation characteristic model is obtained; according to the photovoltaic equipment technical parameters, meteorological data and energy storage equipment technical parameters of step 1, the source storage equipment operation characteristic model is obtained; Step 3: Construct a source-reservoir multi-objective collaborative control model: The source-reservoir multi-objective collaborative control model includes a source-reservoir collaborative control optimization strategy and a source-reservoir collaborative operation constraint strategy. The source-storage collaborative control optimization strategy includes minimizing daily electricity costs, optimizing average hourly power stability, and maximizing daily carbon emission reduction; The source-storage coordinated operation constraint strategy includes the residential load boundary, the photovoltaic reverse power boundary and the energy storage operation boundary; Step 4: Use the chaotic particle swarm algorithm as the optimization algorithm to solve the source-reservoir multi-objective collaborative control model in step 3 and obtain the optimal solution for the source-reservoir multi-objective collaborative control.

2. The multi-objective coordinated control method for source storage for residential users according to claim 1, characterized in that: The power load operation characteristic model in step 2 includes a rigid load curve, a shiftable load curve and a curtailable load curve, and the source storage equipment operation characteristic model in step 2 includes a photovoltaic power generation curve and an energy storage operation characteristic curve.

3. The multi-objective coordinated control method for source storage of residential users according to claim 2 is characterized in that: The rigid load curve is expressed by the following formula: P ri' (t)=P ri (t)·X ri (t) Where P ri' (t) is the actual rigid load power during period t, P ri (t) is the rigid load power in a given period of time t, X ri (t) is the actual working state of the rigid load; The translatable load curve is represented by the following formula: P tr' (t)=P tr (t)·X tr (t) Where P tr' (t) is the actual load power that can be transferred during period t, P tr (t) is the load power that can be shifted in a given period of time, X tr (t) is the actual working state of the load that can be translated during period t; The curtailable load curve is expressed by the following formula: P cut' (t)=P cut (t)·(1-X cut (t)) Where P cut' (t) is the load power that can be actually reduced during period t, P cut (t) is the load power that can be reduced in a given period of time, X cut (t) is the reducibility factor; The photovoltaic power generation curve is expressed by the following formula: P pv-out (t)=η inv ·P PV (t) Where P pv (t) is the photovoltaic power generation power in period t, P pv-out (t) is the photovoltaic output power during period t, P stc is the maximum output power of the photovoltaic cell under given standard conditions, K stc is the light intensity, T stc is temperature, η C is the conversion efficiency of the solar panel, K C (t) is the solar illumination intensity during period t, T(t) is the temperature of the environment where the photovoltaic cell is located, η inv is the photovoltaic inverter efficiency; The energy storage operation characteristic curve is expressed by the following formula: SOC(t+1)=Q(t+1) / Q ed P L (t)=P ri' (t)+P tr' (t)+P cut' (t) In the formula, SQC(t+1) is the state of charge of the energy storage battery at time t+1, Q(t) is the power of a given energy storage battery at time t, Q(t+1) is the power of a given energy storage battery at time t+1, and Q ed is the rated capacity of the energy storage battery, P ch (t) is the charging power of the energy storage battery during period t, P disc (t) is the discharge power of the energy storage battery during period t, P pv-out (t) is the photovoltaic output power for a given period of time, P L (t) is the load power for a given period of time, P ch-max is the maximum charging power of the energy storage battery, P disc-max is the maximum discharge power of the energy storage battery.

4. The multi-objective coordinated control method for source storage for residential users according to claim 1, characterized in that: The objective function minC in step 3 that minimizes the daily electricity cost total It is expressed by the following formula: minC total =C buy -C cell +C bat +C pv Where T is the given scheduling period, c buy (t) is the electricity purchase cost of the upper power grid during period t, c cell (t) is the electricity sales fee of the upper power grid during period t, P buy (t) is the power purchased by the upper power grid during period t, P cell (t) is the power sold by the upper power grid during period t, K bat K is the energy storage battery operation cost coefficient, pv is the photovoltaic operation cost coefficient, Δt is the time period, C buy and C cell is the electricity purchase and sales costs from the upper grid in a given dispatch period, C bat is the cost of energy storage in a given dispatch cycle, C pv is the cost input of PV in a given dispatch period; The objective function for optimizing the average hourly power stability in step 3 is expressed as follows: P load (t)=P ri' (t)+P tr' (t)+P cut' (t)+P ch (t) P supply (t)=P pv-out (t)+P disc (t)+P grid (t) Where P load (t) is the total load of residential users in period t, P supply (t) is the total power supply power in period t, P ri' (t) is the actual rigid load power during period t, P tr' (t) is the actual load power that can be transferred during period t, P cut' (t) is the load power that can be actually reduced during period t, P ch (t) is the charging power of the energy storage battery during period t, P pv-out (t) is the photovoltaic output power for a given period of time, P disc (t) is the discharge power of the energy storage battery during period t, P grid (t) is the power supply power given by the power grid company, and T is the given dispatching period; The objective function maxER for maximizing daily carbon emission reduction in step 3 is expressed as follows: Where T is the given scheduling period, P pv (t) is the photovoltaic power generation power in period t, Δt is the time period, EF grid,t is the real-time dynamic carbon factor obtained from the carbon meter, P buy (t) is the purchased power at time t obtained by the smart meter and the electricity consumption information collection system, P EB-buy (t) is the historical baseline purchased power at the same time obtained by the smart meter and the electricity consumption information collection system.

5. The source-storage multi-objective collaborative control method for residential users according to claim 1, characterized in that: The residential load boundary in step 3 is expressed as follows: t tr-end -t tr-str +1=T tr 0≤X cut (t)≤1 Where, t ri-str and t ri-end are the starting and ending working time of a given rigid load, t tr-str and t tr-end are the start and end working time of the movable load, T tr is the working time of the shiftable load, and the load factor range that can be reduced is [0,1], X ri (t) is the rigid load working state, X tr (t) is the working state of the translatable load, X cut (t) is the reducible load factor; The photovoltaic reverse power boundary is expressed by the following formula: P pv-out (t)-P load (t)≤P grid-max Where P grid-max is the maximum PV reverse power specified by a given grid, P pv-out (t) is the photovoltaic output power during period t, P load (t) is the total load of residential users during period t; The energy storage operation boundary is expressed by the following formula: 0≤P ch (t)≤P ch-max 0≤P disc (t)≤P disc-max SOC min ≤SOC(t+1)≤SOC max Where P ch-max is the maximum charging power of a given energy storage battery, P disc-max is the maximum discharge power of a given energy storage battery, SOC min The minimum state of charge of the energy storage battery, SOC max is the maximum state of charge value of the energy storage battery, P ch (t) is the charging power of the energy storage battery, P disc (t) is the discharge power of the energy storage battery, and SOC(t+1) is the state of charge of the energy storage battery.

6. The multi-objective coordinated control method for source storage of residential users according to claim 1, characterized in that: In step 4, the chaotic particle swarm algorithm is used as the optimization algorithm to solve the power load and source storage equipment operation characteristic model in step 2 and the source storage multi-objective collaborative control model in step 3, including the following steps: Step 41: Generate chaotic particles x using Logistic image n , determine the maximum number of iterations, the number of particles, and initialize the position and velocity of the particles; Step 42: Calculate the fitness value of each particle: For each particle, calculate its fitness value according to the objective function, and record the initial position of the particle as p best , the fitness value of each particle at the current position and p best If the fitness value of the current position is better, the particle's p is updated. best is the current location; Step 43: Traverse the p of all particles best , find the particle with the best fitness value and store its position in g best At the same time, the value is stored in the global optimal value fg best middle,; Step 44: Update the particle's position and velocity using the following formula: v id (i+1)=v id (i)+c1·r1·(p best (i)-x id (i))+c2·r2·(g best (i)-x id (i)) x id (i+1)=x id (i)+v id (i+1) In the formula, v id (i) and x id (i) are the velocity and current position of the particle at the i-th iteration, r1 and r2 are random numbers on [0,1], c1 and c2 are learning factors, and p best (i) represents the individual optimal position of the particle in the i-th iteration, g best (i) represents the position of the particle with the best fitness in the i-th iteration group, and i represents the number of iterations; Step 45: For the optimal position g best =(g best (1),g best (2),...,g best (D)) Perform chaos optimization and set g best (i), i = 1, 2, 3, ..., D is mapped to the interval [0, 1], where D represents the number of decision variables; The chaotic variable sequence is obtained using the following formula: x n+1 =µ·x n ·(1-x n ),n=1,2,…,N In the formula, μ is the chaos parameter, N is the number of particles; By mapping back to the original solution space, the fitness value of each feasible solution is calculated to obtain the optimal feasible solution g * best , replace the position of particles in the current group; Step 46: Determine whether the number of iterations has reached the maximum value, and if so, output the optimal solution fg best , otherwise repeat steps 42 to 46.

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