A joint optimization scheduling method for electric vehicle charging network and distributed energy

Abstract

The present invention relates to a joint optimization scheduling method for electric vehicle charging network and distributed energy, comprising the following steps: step 1, collecting data samples, including electric vehicle load data and distributed energy output data; step 2, constructing a joint optimization scheduling model, setting objective functions and constraints with the goal of maximizing renewable energy consumption, so as to obtain an optimal electric vehicle scheduling plan; step 3, solving the joint optimization scheduling model based on a particle swarm algorithm; step 4, determining the optimal scheduling method according to the output result of step 3. The present invention can achieve maximum renewable energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle energy dispatching, and in particular to a method for joint optimization dispatching of an electric vehicle charging network and distributed energy. Background Art

[0002] In recent years, electric vehicles have rapidly developed into an important adjustable load resource on the power demand side. Due to their scheduling flexibility, many studies have used electric vehicles as virtual energy storage to assist scheduling and have achieved certain results. Distributed energy is an important force in promoting the development of new energy, but its large output volatility and weak adjustable ability limit its development. Consider using the adjustable characteristics of electric vehicles to cooperate with distributed energy generation to complete distributed energy consumption. Based on this, the present invention designs a joint optimization scheduling method for electric vehicle charging networks and distributed energy. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for jointly optimizing the scheduling of electric vehicle charging networks and distributed energy, which can achieve a scheduling method that maximizes the consumption of renewable energy.

[0004] The present invention solves the technical problem by adopting the following technical solutions:

[0005] A method for joint optimization scheduling of electric vehicle charging network and distributed energy, comprising the following steps:

[0006] Step 1: Collect data samples, including electric vehicle load data and distributed energy output data;

[0007] Step 2: Construct a joint optimization scheduling model to set the objective function and constraints with the goal of maximizing renewable energy consumption to obtain the optimal electric vehicle scheduling plan;

[0008] Step 3: Solve the joint optimization scheduling model based on the particle swarm algorithm;

[0009] Step 4: Determine the optimal scheduling method based on the output results of step 3.

[0010] Moreover, the specific method of step 1 is: collecting 96 points of electric vehicle load data, wind power generation data and photovoltaic power generation data samples on a typical day.

[0011] Moreover, the specific steps of step 2 include:

[0012] (1) Constructing the objective function

[0013] According to the goal of maximizing renewable energy consumption, the output of electric vehicles and gas units is optimized and calculated according to 96 points; the objective function is:

[0014]

[0015] Among them, P pv_i is the photovoltaic power generation at the i-th moment, P w_i is the wind power generation at the i-th moment, and Δt is the time interval duration.

[0016] (2) Constructing constraints

[0017] 1) Power balance constraints

[0018] P load_i +P ev_i =P g_i +P pv_i +P w_i (2)

[0019] Among them, P load_i is the load power of purchased electricity at the i-th moment, P ev_i is the load power of the electric vehicle system at the i-th moment.

[0020] 2) Gas turbine output constraints

[0021] |P g_(i+1) -P g_i |≤R·Q g (3)

[0022] Where P g_i is the power generation capacity of the gas power plant at the i-th moment, P g_(i+1) is the power generation capacity of the gas power plant at the i+1th moment, R is the maximum ramp rate of the gas power plant, Q g is the total installed capacity of gas-fired power plants.

[0023] 3) Electric vehicle charging and discharging constraints

[0024] P ev_i ≤N t ·P ev_in (4)

[0025]

[0026] SOC t+1 -SOC t ≥SOC demand_t (6)

[0027] Among them, P ev_in is the maximum charging power of a single electric vehicle, SOC t is the total state of charge of the electric vehicle at the i-th moment, SOC t+1 At the i+1th moment, the total state of charge of the electric vehicle, SOCdemand_t The charge change required by the electric vehicle from time t to time t+1;

[0028] 4) Wind and solar power consumption constraints

[0029] Q pv_i +Q w_i =Q o_i -Q ev_i -Q g_i (7)

[0030] Among them, Q pv_i is the electricity sold by the photovoltaic power plant at the i-th moment, Q w_i is the electricity sold by the wind farm at the i-th moment, Q o_i is the electricity sold by the microgrid at the i-th moment, Q ev_i is the electricity sold by electric vehicles at the i-th moment, Q g_i is the electricity sold by the gas unit at the i-th moment.

[0031] Moreover, the specific steps of step 3 include:

[0032] (1) Parameter determination: Initialize the particle swarm algorithm parameters, including population size, initial position, and initial velocity;

[0033] (2) Fitness value calculation: Calculate the fitness value of each particle and update the position and velocity of the particle; the fitness value is the daily wind power and photovoltaic power consumption under a certain charging and discharging level, and the calculation formula is as follows (8):

[0034]

[0035] The speed update formula is formula (9):

[0036] V k+1 =w*V k +c1*rand()*(p best -X k )+c2*rand()*(g best -X k ) (9)

[0037] Among them, V k+1 is the updated speed; w is the inertia factor; V k is the current speed; rand() is a random number generation function that generates a random number between 0 and 1; p best is the optimal position currently achieved by the particle; X k is the current position of the particle; c1 and c2 are learning factors, which learn from the best historical position of the particle and the best current position in the population respectively;

[0038] The position update formula is formula (10):

[0039] X k+1 =X k +V k+1 (10)

[0040] (3) Solve the optimal scheduling strategy through the particle swarm algorithm: if the maximum number of iterations is met, exit; otherwise, return to step (2) to continue the calculation.

[0041] Set the maximum number of iterations to K. If the maximum number of iterations is not reached, return to step (2) to further calculate the fitness value of each particle and update the position and velocity of the particle.

[0042] The advantages and positive effects of the present invention are:

[0043] In view of the current lack of joint dispatching methods for electric vehicle charging networks and distributed energy, the present invention proposes a joint optimization dispatching method for electric vehicle charging networks and distributed energy in view of the dispatchability of electric vehicles and the volatility of distributed energy. In view of the needs of electric vehicles and distributed energy customers, it is proposed to design an objective function with the goal of maximizing the consumption of renewable energy; to construct constraint functions of power balance, gas turbine output and electric vehicle charging and discharging in view of the dispatchability of electric vehicles and the volatility of distributed energy; and to solve the problem using a particle swarm algorithm to obtain the optimal 96-point electric vehicle charging and discharging dispatching plan, and finally to achieve a dispatching method that maximizes the consumption of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a processing flow chart of the present invention. DETAILED DESCRIPTION

[0045] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0046] A joint optimization scheduling method for electric vehicle charging network and distributed energy, such as Figure 1 As shown, the following steps are included:

[0047] Step 1: Collect data samples, including electric vehicle load data and distributed energy output data;

[0048] The specific method of step 1 is: collecting 96 points of electric vehicle load data, wind power generation data and photovoltaic power generation data samples on a typical day.

[0049] Step 2: Construct a joint optimization scheduling model to set the objective function and constraints with the goal of maximizing renewable energy consumption to obtain the optimal electric vehicle scheduling plan;

[0050] The specific steps of step 2 include:

[0051] (1) Constructing the objective function

[0052] According to the goal of maximizing renewable energy consumption, the output of electric vehicles and gas units is optimized and calculated according to 96 points; the objective function is:

[0053]

[0054] Among them, P pv_i is the photovoltaic power generation at the i-th moment, P w_i is the wind power generation at the i-th moment, and Δt is the time interval duration.

[0055] (2) Constructing constraints

[0056] 1) Power balance constraints

[0057] P load_i +P ev_i =P g_i +P pv_i +P w_i (2)

[0058] Among them, P load_i is the load power of purchased electricity at the i-th moment, P ev_i is the load power of the electric vehicle system at the i-th moment.

[0059] 2) Gas turbine output constraints

[0060] |P g_(i+1) -P g_i |≤R·Q g (3)

[0061] Where P g_i is the power generation capacity of the gas power plant at the i-th moment, P g_(i+1) is the power generation capacity of the gas power plant at the i+1th moment, R is the maximum ramp rate of the gas power plant, Q g is the total installed capacity of gas-fired power plants.

[0062] 3) Electric vehicle charging and discharging constraints

[0063] P ev_i ≤N t ·P ev_in (4)

[0064]

[0065] SOC t+1 -SOC t ≥SOC demand_t (6)

[0066] Among them, Pev_in is the maximum charging power of a single electric vehicle, SOC t is the total state of charge of the electric vehicle at the i-th moment, SOC t+1 At the i+1th moment, the total state of charge of the electric vehicle, SOC demand_t It is the charge change required by the electric vehicle from time t to time t+1.

[0067] 4) Wind and solar power consumption constraints

[0068] Q pv_i +Q w_i =Q o_i -Q ev_i -Q g_i (7)

[0069] Among them, Q pv_i is the electricity sold by the photovoltaic power plant at the i-th moment, Q w_i is the electricity sold by the wind farm at the i-th moment, Q o_i is the electricity sold by the microgrid at the i-th moment, Q ev_i is the electricity sold by electric vehicles at the i-th moment, Q g_i is the electricity sold by the gas unit at the i-th moment.

[0070] Step 3: Solve the joint optimization scheduling model based on the particle swarm algorithm;

[0071] The specific steps of step 3 include:

[0072] (1) Parameter determination: Initialize the particle swarm algorithm parameters, including population size, initial position, and initial velocity;

[0073] In this embodiment, the initial position X0, initial speed V0, etc. are randomly generated, and the population size is set to 5. The initial position represents the initial 96-point charge and discharge power conditions, and the initial speed is the power change step, which is set not to exceed 1.

[0074] (2) Fitness value calculation: Calculate the fitness value of each particle and update the position and velocity of the particle;

[0075] In this embodiment, the adaptation value is the daily wind power and photovoltaic power consumption under a certain charging and discharging level, and the calculation formula is as follows (8):

[0076]

[0077] The speed update formula is formula (9):

[0078] V k+1 =w*V k +c1*rand()*(p best -X k)+c2*rand()*(g best -X k ) (9)

[0079] Among them, V k+1 is the updated speed; w is the inertia factor; V k is the current speed; rand() is a random number generation function that generates a random number between 0 and 1; p best is the optimal position currently achieved by the particle; X k is the current position of the particle; c1 and c2 are learning factors, which learn from the best historical position of the particle and the best current position in the population respectively;

[0080] The position update formula is formula (10):

[0081] X k+1 =X k +V k+1 (10)

[0082] (3) Solve the optimal scheduling strategy through the particle swarm algorithm: if the maximum number of iterations is met, exit; otherwise, return to step (2) to continue the calculation.

[0083] Set the maximum number of iterations to K. If the maximum number of iterations is not reached, return to step (2) to further calculate the fitness value of each particle and update the position and velocity of the particle.

[0084] Step 4: Determine the optimal scheduling method based on the output results of step 3.

[0085] The particle swarm algorithm is used to solve the model to obtain the optimal electric vehicle scheduling and gas unit scheduling strategy, and the optimal scheduling plan is formed based on this strategy.

[0086] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for joint optimization and scheduling of electric vehicle charging network and distributed energy, characterized in that: The following steps are involved: Step 1: Collect data samples, including electric vehicle load data and distributed energy output data; Step 2: Construct a joint optimization scheduling model to set the objective function and constraints with the goal of maximizing renewable energy consumption to obtain the optimal electric vehicle scheduling plan; Step 3: Solve the joint optimization scheduling model based on the particle swarm algorithm; Step 4: Determine the optimal scheduling method according to the output result of step 3; The specific method of step 1 is: collecting 96 points of electric vehicle load data, wind power generation data and photovoltaic power generation data samples on a typical day; The specific steps of step 2 include: (1) Constructing the objective function According to the goal of maximizing renewable energy consumption, the output of electric vehicles and gas units is optimized and calculated according to 96 points; the objective function is: Among them, P pv_i is the photovoltaic power generation power at the i-th moment, P w_i is the wind power generation at the i-th moment, Δt is the time interval duration; (2) Constructing constraints 1) Power balance constraints P load_i +P ev_i =P g_i +P pv_i +P w_i (2) Among them, P load_i is the load power of purchased electricity at the i-th moment, P ev_i is the load power of the electric vehicle system at the i-th moment; 2) Gas turbine output constraints: |P g_(i+1) -P g_i |≤R·Q g (3) Where P g_i is the power generation capacity of the gas power plant at the i-th moment, P g_(i+1) is the power generation of the gas power plant at the i+1th moment, R is the maximum ramp rate of the gas power plant, Q g is the total installed capacity of gas-fired power plants; 3) Electric vehicle charging and discharging constraints P ev_i ≤N t ·P ev_in (4) SOCIETY t+1 -SOC t ≥SOC demand_t (6) Among them, P ev_in is the maximum charging power of a single electric vehicle, SOC t is the total state of charge of the electric vehicle at the i-th moment, SOC t+1 At the i+1th moment, the total state of charge of the electric vehicle, SOC demand_t The charge change required by the electric vehicle from time t to time t+1; 4) Wind and solar power consumption constraints Q pv_i +Q w_i =Q o_i -Q ev_i -Q g_i (7) Among them, Q pv_i is the electricity sold by the photovoltaic power plant at the i-th moment, Q w_i is the electricity sold by the wind farm at the i-th moment, Q o_i is the electricity sold by the microgrid at the i-th moment, Q ev_i is the electricity sold by electric vehicles at the i-th moment, Q g_i is the electricity sold by the gas generator set at the i-th moment; The specific steps of step 3 include: (1) Parameter determination: Initialize the particle swarm algorithm parameters, including population size, initial position, and initial velocity; (2) Fitness value calculation: Calculate the fitness value of each particle and update the position and velocity of the particle; The adaptation value is the daily wind power and photovoltaic power consumption under a certain charging and discharging level, and the calculation formula is as follows (8): The speed update formula is formula (9): V k+1 = w*V k + c1 * rand()* (p best - X k )+ c2 *rand()*(g best -X k ) (9) Among them, V k+1 is the updated speed; w is the inertia factor; V k is the current speed; rand() is a random number generation function that generates a random number between 0 and 1; p best is the optimal position currently achieved by the particle; X k is the current position of the particle; c1 and c2 are learning factors, which learn from the best historical position of the particle and the best current position in the population respectively; The position update formula is formula (10): X k+1 =X k + V k+1 (10) (3) Solve the optimal scheduling strategy through the particle swarm algorithm: if the maximum number of iterations is met, exit; otherwise, return to step (2) to continue the calculation; Set the maximum number of iterations to K. If the maximum number of iterations is not reached, return to step (2) to further calculate the fitness value of each particle and update the position and velocity of the particle.

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