Running control method of wind and light storage micro-grid system based on carbon emission cost
By applying particle swarm algorithm to optimize operating parameters in the wind and light storage microgrid system, the problems of operating costs and carbon emissions of wind and light storage microgrid system under the background of the carbon trading market are solved, and the reduction of carbon emissions and the balance between power supply and demand has been achieved, which has improved the Economic benefits and penetration rate of new energy.
Patent Information
- Application Number
- CN202411453453.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-02-21
AI Technical Summary
In the context of the carbon trading market, the wind and light storage microgrid system may face the problems of wind and light abandonment and insufficient power supply during operation, and the existing energy storage research is relatively limited, and the optimization role of the carbon trading market has not been fully utilized.
Using the operation control method based on particle swarm algorithm, by obtaining the equivalent carbon emissions and operating costs of the wind and light storage microgrid system, the parameters such as power purchase, power sales, energy storage and load cutting are optimized to minimize the operating costs and consider Impact of carbon emission costs.
It effectively reduces carbon emissions, supports the integration of renewable energy, balances power supply and demand, reduces operating costs, improves economic benefits, and increases the penetration rate of new energy.
Smart Images
Figure CN119496111A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of smart grids. More specifically, the present invention relates to an operation control method of a wind, solar and energy storage microgrid system based on carbon emission costs. Background Art
[0002] Excessive emission of greenhouse gases has aggravated the greenhouse effect and has had a negative impact on the global climate. As the main greenhouse gas, reducing carbon dioxide emissions is seen as the key to solving climate problems. The new power system with new energy as the core has become a key measure to achieve carbon neutrality, while promoting renewable clean energy such as wind power and photovoltaics to become the main direction of development of the power industry.
[0003] Wind and solar power generation have brought significant environmental benefits, but due to their natural characteristics, they may be abandoned and power supply may be insufficient during operation. Energy storage systems can improve the absorption capacity of new energy and the reliability of power supply through flexible adjustment capabilities.
[0004] With the official launch of my country's carbon market, various regions have started carbon emission trading pilots and launched a variety of carbon trading products. Current energy storage research mainly focuses on the direct economic benefits of energy storage, while research on the optimization role of energy storage in the context of the carbon trading market is relatively limited. Summary of the invention
[0005] The present invention provides an operation control method for a wind, solar and energy storage microgrid system based on carbon emission costs, aiming to improve the above-mentioned problems.
[0006] The present invention is implemented as follows: an operation control method of a wind-solar-storage microgrid system based on carbon emission costs, the method comprising the following steps:
[0007] (1) Obtain the actual equivalent carbon emissions Q1 of the wind, solar and storage microgrid system and calculate the current carbon emission cost
[0008] (2) Power purchase cost F based on wind, solar and storage microgrid system buy , electricity sales revenue F sell , Carbon emission costs Energy storage cost F ESS and load reduction cost F curt The operating costs of building a wind, solar and storage microgrid system;
[0009] (3) The operating parameters with the minimum operating cost in the current cycle are obtained based on the particle swarm algorithm, and the wind-solar-storage microgrid system operates based on the operating parameters in the current cycle.
[0010] Furthermore, the calculation formula of the operating cost F of the wind-solar-storage microgrid system is as follows:
[0011]
[0012] Among them, F buy F is the electricity purchase cost of the wind-solar-storage microgrid system from the upper power grid; sell The revenue obtained by the wind-solar-storage microgrid system from selling electricity to the upper power grid; F ESS The cost required to charge and discharge the energy storage battery in the wind-solar-storage microgrid system; F curt Reduce load costs for wind, solar and storage microgrid systems.
[0013] Furthermore, the cost of carbon emissions The calculation formula is as follows:
[0014]
[0015] Where: C1, C2, C3, C4 are carbon emission cost coefficients; Q0, Q 01 , Q 02 is the boundary amount of carbon emission cost.
[0016] Furthermore, the electricity purchase cost F buy The calculation formula is as follows:
[0017]
[0018] Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P grid (t) The exchange power between the wind-solar-storage microgrid system and the upper power grid in the tth dispatching period, C buy (t) is the unit electricity purchase cost in the tth dispatch period.
[0019] Furthermore, the electricity sales revenue F sell The calculation formula is as follows:
[0020]
[0021] Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P grid (t) The exchange power between the wind-solar-storage microgrid system and the upper power grid in the tth dispatching period, C sell (t) is the unit electricity sales cost in the tth dispatch period.
[0022] Furthermore, the cost of charging and discharging the energy storage battery is ESS The calculation formula is as follows:
[0023]
[0024] Where T is the total number of scheduling time periods in a cycle, and Δt represents the duration of the scheduling period P ESS (t) is the exchange power between the energy storage battery and the bus in the tth dispatch period, C ESS is the unit operating cost of the energy storage battery.
[0025] Furthermore, the load cost F curt The calculation formula is as follows:
[0026]
[0027] Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P cut is the cutting load, C cut Reduce unit load costs.
[0028] Furthermore, the inertia weight ω in the particle velocity update formula is set to a dynamic value, and the formula for calculating the inertia weight ω is as follows:
[0029] ω=ω max -t n (ω max -ω min ) / T max
[0030] Where, t n is the current iteration number, T max is the maximum number of iterations, ω min and ω max Represent the minimum and maximum values of the inertia weight respectively.
[0031] Furthermore, the calculation formulas for the learning factors c1 and c2 in the particle velocity update formula are as follows:
[0032]
[0033]
[0034] Where, t n and t max Represent the current number of iterations and the maximum number of iterations respectively.
[0035] The present invention proposes a microgrid energy storage operation optimization method that takes into account carbon emission costs. By reducing dependence on fossil fuels, it can effectively reduce carbon emissions and support the effective integration of renewable energy. This optimization can balance electricity supply and demand, reduce operating costs, and improve economic benefits. In addition, the provision of energy storage batteries helps to increase the penetration rate of new energy and reduce the amount of electricity purchased from the superior power grid. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 A schematic diagram of the structure of a wind-solar-storage microgrid system provided by an embodiment of the present invention;
[0037] Figure 2 A flow chart of the operation control method of a wind-solar-storage microgrid system based on carbon emission costs provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0038] The specific implementation modes of the present invention are further explained in detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention.
[0039] The wind-solar-storage microgrid system mainly relies on clean energy such as wind power and photovoltaics to provide electricity. The energy storage battery is responsible for adjusting the power balance. At the same time, the upper power grid also participates in the regulation to ensure the stable power supply of the micro-inductor network load. The structural diagram is as follows Figure 1 As shown in the figure, the wind, solar and storage microgrid system includes:
[0040] Photovoltaic power generation group is connected to the busbar through the photovoltaic grid-connected controller; wind turbine generator is connected to the busbar through the wind turbine grid-connected controller; energy storage battery is connected to the busbar through the energy storage grid-connected controller; microgrid load is connected to the busbar,
[0041] The electric energy generated by the photovoltaic power generation group and the wind turbine generator is supplied to the microgrid load through the bus. When the electric energy generated by the photovoltaic power generation group and the wind turbine generator is more than the electric energy required by the microgrid load, the excess electric energy is stored in the energy storage battery or connected to the upper-level power grid. When the electric energy generated by the photovoltaic power generation group and the wind turbine generator is not enough to meet the electric energy required by the microgrid load, the microgrid load is supplied with power through the energy storage battery or the upper-level power grid.
[0042] Figure 2 A flow chart of an operation control method of a wind-solar-storage microgrid system based on carbon emission costs provided by an embodiment of the present invention, the method is specifically as follows:
[0043] (1) Obtain the actual equivalent carbon emissions Q1 of the wind, solar and storage microgrid system and calculate the current carbon emission cost
[0044] Determine the carbon emission quota Q0 of the wind-solar-storage microgrid system. If the actual equivalent carbon emissions Q1 is lower than Q0, the wind-solar-storage microgrid system can sell the remaining carbon emission quota to obtain income; otherwise, the wind-solar-storage microgrid system needs to purchase additional carbon emission quotas, and the larger the purchase amount, the higher the unit cost, and the carbon emission cost The calculation formula is as follows:
[0045]
[0046] Where: is the carbon emission cost; C1, C2, C3, C4 are carbon emission cost coefficients; Q0, Q 01 , Q 02 is the boundary amount of carbon emission cost.
[0047] (2) The operating cost of the wind-solar-storage microgrid system is constructed based on the power purchase cost, power sales revenue, carbon emission cost, energy storage cost and load reduction cost of the wind-solar-storage microgrid system;
[0048] (3) The operating parameters with the minimum operating cost in the current cycle are obtained based on the particle swarm algorithm, and the wind-solar-storage microgrid system operates based on the operating parameters in the current cycle.
[0049] The cost of operating the energy storage battery, the cost of carbon emissions, the cost of load reduction, and the cost of selling electricity to the external distribution network are constructed
[0050]
[0051] Among them, F buy The cost of purchasing electricity from the upper power grid for the wind, solar and storage microgrid system;
[0052] F sell The revenue from selling electricity to the upper grid for the wind, solar and storage microgrid system;
[0053] F ESS The cost required to charge and discharge the energy storage batteries in the wind, solar and storage microgrid system;
[0054] F curt Reduce the load cost for wind, solar and storage microgrid systems;
[0055] (a) Electricity purchase cost F buy The calculation formula is as follows:
[0056]
[0057] Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P grid (t) The exchange power between the wind-solar-storage microgrid system and the upper-level grid in the t-th dispatching period, including the exchange power when the wind-solar-storage microgrid system purchases electricity from the upper-level grid or sells electricity to the upper-level grid, C buy (t) is the unit electricity purchase cost in the tth dispatch period.
[0058] (b) Electricity sales revenue F sell The calculation formula is as follows:
[0059]
[0060] In the formula, C sell(t) is the unit electricity sales cost in the tth dispatch period.
[0061] (c) The cost of charging and discharging the energy storage battery F ESS The calculation formula is as follows:
[0062]
[0063] Where P ESS (t) is the exchange power between the energy storage battery and the bus in the tth scheduling period, that is, the charging power or discharging power of the energy storage battery, C ESS is the unit operating cost of the energy storage battery.
[0064] (d) Load reduction cost F curt The calculation formula is as follows:
[0065]
[0066] Where P cut is the cutting load, C cut Reduce unit load costs.
[0067] The present invention uses the exchange power P between the wind-solar-storage microgrid system and the upper power grid grid (t), the exchange power P between the energy storage battery and the bus ESS And cutting load P cut are variables, and the constraints include: power and capacity constraints of the energy storage battery, exchange power constraints between the upper power grid and the microgrid system, and power balance constraints.
[0068] The capacity constraints of energy storage batteries are as follows:
[0069]
[0070] Where, E(t) and E(t-1) are the energy storage battery power in the t-th and t-1-th scheduling periods respectively, ε is the self-discharge rate of the energy storage battery, are the charging power and discharging power of the energy storage battery in the tth scheduling period, η ch , η dis They are the charging efficiency and discharging efficiency of the energy storage battery respectively.
[0071] E min ≤E(t)≤E max (8)
[0072] In the formula, E min 、E max They are the lower and upper limits of the energy storage battery capacity respectively.
[0073]
[0074] In the formula, are the lower and upper limits of the energy storage battery charging power, respectively. They are respectively the lower limit and upper limit of the discharge power of the energy storage battery.
[0075] Power purchase and sale P grid (t) The constraints are as follows:
[0076] P grid.min ≤P grid (t)≤P grid.max (10)
[0077] Where P grid.min , P grid.max They are respectively the minimum and maximum values of the power exchange allowed between the wind, solar and energy storage microgrid system and the upper-level power grid.
[0078] The power balance constraints are as follows:
[0079]
[0080] Where: P W (t), P PV (t) are the wind turbine output and photovoltaic output at the t-th scheduling period, respectively, P load (t) is the load power in the tth scheduling period.
[0081] At any time, the power output must match the load. When there is excess electricity, wind and solar power must be abandoned; when there is insufficient electricity, measures to reduce the load must be taken.
[0082] Cuttable load constraint: 0≤P cut (t)≤P cut.max , where P cut.max The maximum load that can be cut.
[0083] The particle swarm optimization algorithm is simple, easy to use, has fast convergence speed and few parameters to set, but it is easy to fall into the local optimal solution. Based on the particle swarm optimization algorithm, the particle weight and acceleration coefficient settings are improved accordingly. The particle speed update formula is:
[0084]
[0085] In the formula, They represent the velocity of the D-dimensional vector of particle I in the k+1th iteration and the kth iteration respectively, ω is the inertia weight, c1 and c2 are both learning factors, They represent two random numbers in the kth iteration, with values of [0,1], represents the D-dimensional vector coordinates corresponding to the position of particle I generated in the kth iteration; Represents the D-dimensional vector coordinates corresponding to the current individual optimal position of particle I, Represents the D-dimensional vector coordinates corresponding to the global optimal position of the entire particle group.
[0086] From formula (12), we can see that the speed of updating to the next generation is composed of the speed of the current particle and the update speed of the two particles, which fully reflects that the particles are both part of the individual learning process and part of the social learning process. The first part With the latter two parts The weight involves the ability to balance local optimality and global optimality.
[0087] The weight of the first part ω is constant, that is, the current particle speed is always 1 in this process, which will have a certain impact on the local search ability. In order to better solve this problem, the inertia weight ω in the speed update formula is changed from a constant value to a dynamic variable.
[0088] The larger the weight setting, the better the performance of the optimization algorithm in the local search, while the smaller the setting, the more effective it is to avoid the divergence in the later stage of iteration, thereby ensuring the stability of the final result. Therefore, the weight ω in the PSO algorithm (particle swarm algorithm) is calculated based on the following formula:
[0089] ω=ω max -t n (ω max -ω min ) / T max (13)
[0090] Where, t n is the current iteration number, T max is the maximum number of iterations set; ω min and ω max Represent the minimum and maximum values of the inertia weight respectively.
[0091] The present invention also introduces sine and cosine functions to construct nonlinear asynchronous learning factors c1 and c2. When c1 is large and c2 is small, the particle swarm algorithm has better global search capabilities. When c1 is small and c2 is large, it has better local search capabilities. In order to control c1 to take a larger value and c2 to take a smaller value in the initial stage to enhance the global search capability, and to let c1 take a smaller value and c2 take a larger value in the final iteration stage to enhance the local search capability, sine and cosine functions are used to control c1 and c2, so that the value of c1 can be nonlinearly reduced and the value of c2 can be nonlinearly increased. The calculation formulas of the learning factors c1 and c2 are as follows:
[0092]
[0093] Where, t n and t max Represent the current number of iterations and the maximum number of iterations respectively.
[0094] By adjusting the particle weights, the PSO algorithm can re-optimize in the initial stage and turn to breadth search for the optimal solution in the later iteration, effectively avoiding the inaccuracy of local search.
[0095] In the embodiment of the present invention, the process of the particle swarm algorithm is as follows:
[0096] (11) Position encoding of the operating parameters, including: the exchange power P between the wind-solar-storage microgrid system and the upper grid grid (t), the exchange power P between the energy storage battery and the bus ESS And cutting load P cut ;
[0097] (12) Setting the number of particles and the number of iterations, generating initial particles, and initializing the local optimal solution and the global optimal solution of the particles;
[0098] (13) Calculate the fitness of the particle swarm based on formula (2), and update the best position of each particle and the best position of the entire swarm;
[0099] (14) Determine whether the maximum number of iterations has been reached. If so, output the operating parameters corresponding to the best position in the entire population. If not, execute step (15).
[0100] (15) Update the particle velocity and position and execute step (13).
[0101] The present invention has been described exemplarily. Obviously, the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.
Claims
1. An operation control method of a wind-solar-storage microgrid system based on carbon emission cost, characterized in that: The method comprises the following steps: (1) Obtain the actual equivalent carbon emissions Q1 of the wind, solar and storage microgrid system and calculate the current carbon emission cost (2) Power purchase cost F based on wind, solar and storage microgrid system buy , electricity sales revenue F sell , Carbon emission costs Energy storage cost F ESS and load reduction cost F curt The operating costs of building a wind, solar and storage microgrid system; (3) The operating parameters with the minimum operating cost in the current cycle are obtained based on the particle swarm algorithm, and the wind-solar-storage microgrid system operates based on the operating parameters in the current cycle.
2. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 1 is characterized in that: The calculation formula for the operating cost F of the wind-solar-storage microgrid system is as follows: Among them, F buy F is the electricity purchase cost of the wind-solar-storage microgrid system from the upper power grid; sell The revenue obtained by the wind-solar-storage microgrid system from selling electricity to the upper power grid; F ESS The cost required to charge and discharge the energy storage battery in the wind-solar-storage microgrid system; F curt Reduce load costs for wind, solar and storage microgrid systems.
3. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 2 is characterized in that: Carbon emissions costs The calculation formula is as follows: Where: C1, C2, C3, C4 are carbon emission cost coefficients; Q0, Q 01 , Q 02 is the boundary amount of carbon emission cost.
4. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 2 is characterized in that: Electricity purchase cost F buy The calculation formula is as follows: Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P grid (t) The exchange power between the wind-solar-storage microgrid system and the upper power grid in the tth dispatching period, C buy (t) is the unit electricity purchase cost in the tth dispatch period.
5. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 2 is characterized in that: Electricity sales revenue F sell The calculation formula is as follows: Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P grid (t) The exchange power between the wind-solar-storage microgrid system and the upper power grid in the tth dispatching period, C sell (t) is the unit electricity sales cost in the tth dispatch period.
6. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 2 is characterized in that: The cost of charging and discharging energy storage batteries F ESS The calculation formula is as follows: Where T is the total number of scheduling time periods in a cycle, and Δt represents the duration of the scheduling period P ESS (t) is the exchange power between the energy storage battery and the bus in the tth dispatch period, C ESS is the unit operating cost of the energy storage battery.
7. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 2 is characterized in that: Load reduction cost F curt The calculation formula is as follows: Where T is the total number of scheduling time periods in a cycle, Δt represents the duration of the scheduling period, and P cut is the cutting load, C cut Reduce unit load costs.
8. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 1 is characterized in that: Set the inertia weight ω in the particle velocity update formula to a dynamic value. The formula for calculating the inertia weight ω is as follows: oh = oh max -t n (oh max -oh min ) / T max Where, t n is the current iteration number, T max is the maximum number of iterations, ω min and ω max Represent the minimum and maximum values of the inertia weight respectively.
9. The operation control method of the wind-solar-storage microgrid system based on carbon emission cost as claimed in claim 1, characterized in that: The calculation formulas for the learning factors c1 and c2 in the particle velocity update formula are as follows: Where, t n and t max Represent the current number of iterations and the maximum number of iterations respectively.