Active power distribution network elastic optimization method with supply and demand cooperation

Abstract

The invention discloses an elastic optimization method for a supply-demand collaborative active power distribution network, and the method comprises the steps: taking three key variables, namely, the output of a distributed power supply, the charging and discharging power of energy storage equipment and the load adjustment amount in the active power distribution network as particles in a particle swarm algorithm; under the constraint conditions of power grid voltage limitation and distributed power supply capacity limitation, a particle swarm optimization algorithm is used for iterative optimization to obtain an optimal supply and demand coordination scheme, so that the flexibility of the active power distribution network is improved while the operation cost of the active power distribution network is minimized, and various faults and disturbance conditions can be effectively handled; the method can effectively balance the operation cost and elastic demand of the active power distribution network, improve the scheduling efficiency, improve the scheduling efficiency and resource utilization efficiency of the active power distribution network, reduce the operation cost, enhance the elasticity of the active power distribution network when facing faults and uncertain factors, reduce the load loss and power failure time, and improve the reliability of the active power distribution network. Therefore, safe and stable operation of the active power distribution network with a novel structure is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network dispatching, and in particular relates to a supply-demand coordinated active distribution network elasticity optimization method. Background Art

[0002] Active distribution network is relative to traditional distribution network. Active distribution network includes a variety of distributed power sources, such as distributed photovoltaic power generation, distributed wind power generation, micro gas turbines, etc. These distributed power sources can actively inject electric energy into the distribution network, changing the mode in which electric energy can only flow from the transmission network to the power user in a single phase in the traditional distribution network, forming a two-way energy exchange between the power source and the load. The supply and demand coordination of active distribution network refers to the coordination and cooperation between the supply and demand of electric energy, and its purpose is to achieve the optimal configuration and efficient and stable operation of various electric energy resources.

[0003] Distributed power sources have the characteristics of flexible power generation, easy installation, less environmental pollution and high economic benefits. In particular, photovoltaic power generation and wind power generation are clean energy sources that the country is vigorously developing in order to promote a low-carbon economy. However, distributed power sources still have many disadvantages. For example, they are greatly affected by natural conditions and have randomness and volatility, which have a great impact on the stability of the distribution network and also bring new variables to the supply and demand coordinated scheduling mechanism of the traditional distribution network, making the supply and demand coordinated scheduling mechanism of the traditional distribution network no longer adapt to the use requirements of the new active distribution network, and there are problems such as low scheduling efficiency and difficulty in balancing cost and flexibility. In order to solve the above problems, it is necessary to develop a supply and demand coordinated active distribution network elasticity optimization method. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a supply-demand coordinated active distribution network elastic optimization method, which has high scheduling efficiency, high resource utilization, low operating cost, strong elastic adjustment capability and good overall operating stability.

[0005] The object of the present invention is achieved by: a method for optimizing the elasticity of an active distribution network with coordinated supply and demand, comprising the following steps:

[0006] S1, collects the output data of distributed power sources of the active distribution network, collects the charging and discharging power data of the energy storage equipment of the active distribution network, and collects the load adjustment data of the active distribution network, where the distributed power sources include photovoltaic power generation and wind power generation;

[0007] S2, using the distributed power output data in the active distribution network, the charging and discharging power data of the energy storage equipment and the load adjustment data as particles in the particle swarm algorithm, and initializing the particles;

[0008] S3, calculates the operating cost and elasticity index of the active distribution network based on the particle positions;

[0009] S4, update the particle's velocity and position, where the velocity update formula is:

[0010]

[0011] In the above formula, is the velocity of the i-th particle in the k+1-th iteration in the d-th dimension; w is the inertia weight; is the speed of the i-th particle in the k-th iteration in the d-th dimension; c1 and c2 are learning factors, also known as acceleration constants, c1 is the step size for adjusting the particle to fly to its own optimal position, and c2 is the step size for adjusting the particle to fly to the global optimal position; r1 and r2 are random numbers uniformly distributed in the interval [0,1]; is the individual optimal position of the ith particle in the dth dimension up to the kth iteration; is the current position of the i-th particle at the k-th iteration in the d-th dimension; is the global optimal position of the entire particle swarm in the dth dimension up to the kth iteration;

[0012] The position update formula is:

[0013]

[0014] In the above formula, is the position of the i-th particle after the k+1-th iteration in the d-th dimension; is the position of the i-th particle at the k-th iteration in the d-th dimension; is the velocity of the i-th particle in the k+1-th iteration in the d-th dimension;

[0015] S5, verify whether the voltage of each node meets the grid voltage limit and whether the output of the distributed power generation meets the capacity limit. If not, make adjustments or give penalties;

[0016] S6, repeat the above steps until the iteration stop condition is met, and the optimal supply and demand coordination solution is obtained, so that the active distribution network has the optimal elasticity while minimizing the operating cost.

[0017] Preferably, the operating cost includes distributed power generation cost, energy storage equipment loss cost and demand response cost, which is the sum of the three.

[0018] Further preferably, the distributed power generation cost includes photovoltaic distributed power generation cost and wind power distributed power generation cost, wherein the calculation formula of photovoltaic distributed power generation cost is:

[0019]

[0020] In the above formula, C pv is the photovoltaic equipment cost coefficient; Ppv Provide power for photovoltaic power;

[0021] The calculation formula for the cost of wind power distributed power generation is:

[0022]

[0023] In the above formula, C w is the cost coefficient of wind power equipment; P w Providing wind power.

[0024] Further preferably, the calculation formula of the energy storage device loss cost is:

[0025]

[0026] In the above formula, C es is the loss cost coefficient of energy storage equipment; P es is the charging and discharging power of the energy storage device; n is the number of charging and discharging times in a certain period of time.

[0027] Further preferably, the calculation formula of the demand response cost is:

[0028] C DR =C d ×|ΔP d |;

[0029] In the above formula, C d The unit compensation price for load users; ΔP d The load adjustment amount for demand response.

[0030] Preferably, the elasticity index includes power supply restoration time after a fault, voltage fluctuation range and power shortage factor, and the elasticity index is constructed by comprehensively considering the three factors.

[0031] Further preferably, the post-fault power supply recovery time refers to the time T from the occurrence of the fault to the recovery of all non-fault areas to the normal power supply voltage range. r , where the normal supply voltage range is ±5% of the rated voltage.

[0032] Further preferably, the voltage fluctuation range refers to the voltage V of each node of the active distribution network. i With rated voltage V n The maximum absolute value of the deviation can be expressed as max(|V i -V n |), where the voltage of each node is V i It is obtained through power flow calculation under various operating conditions, and the operating conditions include normal operating conditions and post-fault recovery processes.

[0033] Further preferably, the power shortage refers to the total load demand P of the active distribution network. load The actual available power P avail The difference between the two can be expressed as ΔP = P load -P avail , where the actual available power P avail It is the sum of the power output of the distributed power source and the discharge power of the energy storage device determined according to the particle position.

[0034] Due to the adoption of the above technical scheme, the beneficial effects of the present invention are as follows: based on the distributed power output of the active distribution network, the charging and discharging power of the energy storage device and the load adjustment data, the present invention adopts a particle swarm algorithm, and takes whether the voltage of each node meets the voltage limit of the power grid and whether the output of the distributed power source meets the capacity as the iteration stop condition, and obtains the optimal supply and demand coordination scheme through repeated iterative calculation, thereby improving the dispatching efficiency and resource utilization efficiency of the active distribution network and reducing the operating cost at the same time; the present invention can effectively balance the operating cost and elasticity demand of the active distribution network through the particle swarm algorithm, and improve the dispatching efficiency; the present invention enhances the elasticity of the active distribution network in the face of faults and uncertain factors through the optimal elasticity index, and reduces load losses and power outage time; the present invention improves the stability of the overall operation of the active distribution network through the optimized integration of distributed power sources, energy storage devices and load resources, and has important practical application value and economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0037] like Figure 1 As shown, the present invention provides a supply-demand coordinated active distribution network elasticity optimization method, comprising the following steps:

[0038] S1, collects the output data of distributed power sources of the active distribution network, collects the charging and discharging power data of the energy storage equipment of the active distribution network, and collects the load adjustment data of the active distribution network. The distributed power sources are mainly the most common photovoltaic power generation and wind power generation;

[0039] S2, using the distributed power output data in the active distribution network, the charging and discharging power data of the energy storage equipment and the load adjustment data as particles in the particle swarm algorithm, and initializing the particles;

[0040] S3, calculates the operating cost and elasticity index of the active distribution network based on the particle positions;

[0041] S4, update the particle's velocity and position, where the velocity update formula is:

[0042]

[0043] In the above formula, is the speed of the i-th particle in the k+1-th iteration in the d-th dimension; w is the inertia weight, which determines the influence of the particle's previous speed on the current speed. Generally speaking, a larger w value is conducive to global search, and a smaller w value is conducive to local search; is the speed of the i-th particle in the k-th iteration in the d-th dimension; c1 and c2 are learning factors, also known as acceleration constants, c1 is the step size for adjusting the particle to fly to its own optimal position, and c2 is the step size for adjusting the particle to fly to the global optimal position; r1 and r2 are random numbers uniformly distributed in the interval [0,1], both of which increase the randomness of the algorithm; is the individual optimal position of the ith particle in the dth dimension up to the kth iteration; is the current position of the i-th particle at the k-th iteration in the d-th dimension; is the global optimal position of the entire particle swarm in the dth dimension up to the kth iteration;

[0044] The position update formula is:

[0045]

[0046] In the above formula, is the position of the i-th particle after the k+1-th iteration in the d-th dimension; is the position of the i-th particle at the k-th iteration in the d-th dimension; is the velocity of the i-th particle in the k+1-th iteration in the d-th dimension;

[0047] S5, verify whether the voltage of each node meets the grid voltage limit and whether the output of the distributed power generation meets the capacity limit. If not, make adjustments or give penalties;

[0048] S6, repeat the above steps until the iteration stop condition is met, and the optimal supply and demand coordination solution is obtained, so that the active distribution network has the optimal elasticity while minimizing the operating cost.

[0049] Particle swarm optimization is an optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks. In the algorithm, each possible solution is regarded as a particle in the search space, and each particle has its own position and velocity.

[0050] The output (i.e., output power) of a distributed power source (such as distributed photovoltaic power, wind power, etc.) is set as a dimension of the particle. For example, in an active distribution network containing multiple distributed power sources, certain dimensions of the particle can represent the output size of each distributed power source.

[0051] Some other dimensions of the particle correspond to the charging and discharging power of the energy storage device. Positive values ​​represent discharging power, and negative values ​​represent charging power. For example, if the value of the particle in this dimension is 8kW at a certain moment, it means that the energy storage device is discharging at a power of 8kW at that moment.

[0052] For adjustable loads, the adjustment amount is also used as the dimension of the particle. For example, the load adjustment range of a commercial building air-conditioning system is -15kW to 15kW (negative means reducing the load, positive means increasing the load). The value of the particle in the corresponding dimension reflects the load adjustment of the air-conditioning system.

[0053] Among them, the operating costs mainly include distributed power generation costs, energy storage equipment loss costs and demand response costs.

[0054] Among them, the distributed power generation cost includes the photovoltaic distributed power generation cost and the wind power distributed power generation cost. The calculation formula of the photovoltaic distributed power generation cost is:

[0055]

[0056] In the above formula, C pv (Yuan / kW) is the cost coefficient of photovoltaic equipment; P pv Produce photovoltaic power.

[0057] Regarding the cost coefficient of photovoltaic equipment, generally speaking, the equipment cost coefficient of a small distributed photovoltaic power station (including equipment depreciation, maintenance and other comprehensive costs) is about 3 to 6 yuan / watt. For example, the initial equipment investment of a 10-kilowatt distributed photovoltaic power station may be around 30,000 to 60,000 yuan, and the corresponding cost coefficient after the annual equipment cost is amortized is within this range. For large centralized photovoltaic power stations, due to the scale effect, the cost coefficient may be lower, about 2 to 4 yuan / watt.

[0058] The calculation formula for the cost of wind power distributed power generation is:

[0059]

[0060] In the above formula, C w is the cost coefficient of wind power equipment; P w Providing wind power.

[0061] Regarding the cost coefficient of wind power equipment, the cost coefficient of onshore wind power equipment is usually around 4 to 7 yuan per watt. For example, a 5-megawatt onshore wind farm has an equipment investment of about 20 to 35 million yuan, and the converted cost coefficient is in this range. However, due to the difficulty of construction and maintenance, the equipment cost coefficient of offshore wind power is relatively high, about 8 to 12 yuan per watt, or even higher. This is because offshore wind power requires special infrastructure support, anti-corrosion measures, and other complex engineering and equipment requirements such as offshore construction.

[0062] Among them, the calculation formula for the loss cost of energy storage equipment is:

[0063]

[0064] In the above formula, C es (yuan / kW) is the loss cost coefficient of energy storage equipment; P es is the charging and discharging power of the energy storage device. Its absolute value is taken because there are losses in both charging and discharging. n is the number of charges and discharges in a certain period of time.

[0065] It is worth noting that assuming the charge and discharge efficiency is θ es , when actually calculating the loss cost of energy storage equipment, if it is a charging process, then the actual power obtained from the active distribution network should be If it is a discharge process, the power actually released into the active distribution network should be P es ×θ es .

[0066] In this way, when calculating the loss cost of energy storage equipment, it is necessary to consider the impact of charging and discharging efficiency on charging and discharging power, which in turn affects the calculation result of the loss cost. Therefore, in the actual calculation process, the above formula should be supplemented with calculations related to charging efficiency, otherwise the calculated loss cost will be inaccurate.

[0067] The calculation formula of demand response cost is:

[0068] C DR =C d ×|ΔP d |;

[0069] In the above formula, C d (Yuan / kW) is the unit compensation electricity price for load users; ΔP d is the load adjustment amount participating in demand response, which is determined by the particle position.

[0070] Therefore, the total operating cost of the active distribution network is

[0071] In addition, the active distribution network may also contain other types of distributed power sources, which can be added to the loss cost calculation based on the actual composition structure.

[0072] Among them, the elasticity indicators mainly refer to the time to restore power supply after a fault, the voltage fluctuation range and the power shortage.

[0073] The power supply recovery time after a fault refers to the time from the occurrence of the fault to the recovery of all non-fault areas to the normal power supply voltage range. r , the shorter the recovery time, the better the resilience of the active distribution network.

[0074] Simulate faults of different locations and types in the active distribution network, such as line faults, distributed power supply faults, etc., operate the active distribution network according to the distributed power supply output, energy storage device charging and discharging power and load adjustment strategy determined by the particle position, and record the time from the occurrence of the fault to the recovery of the non-fault area to the normal power supply voltage range (such as ±5% of the rated voltage), which is T r .

[0075] Among them, the voltage fluctuation range refers to the voltage V at each node of the active distribution network. i With rated voltage V n The maximum absolute value of the deviation can be expressed as max(|V i -V n |), the smaller the voltage fluctuation range, the better the flexibility of the active distribution network.

[0076] Under various operating conditions, such as normal operation and post-fault recovery, the voltage V of each node in the active distribution network is obtained through power flow calculation. i .

[0077] Among them, the power shortage refers to the total load demand P of the active distribution network. load The actual available power P avail The difference between the two can be expressed as ΔP = P load -P avail The smaller the power deficit, the closer the power that the active distribution network can provide in the event of a fault is to the load demand, and the better the flexibility is.

[0078] Actual available power P avail It is the sum of the power determined by the position of the particle, including the output of distributed power sources and the discharge power of energy storage devices.

[0079] Therefore, the elasticity index E can be constructed by combining the above factors, such as using the following expression:

[0080]

[0081] In the above formula, α and β are weight coefficients, which should be adjusted according to the importance attached to different factors.

[0082] In the actual application process, the particles are initialized first. At the beginning of the algorithm, the position and speed of the particles are randomly initialized according to the actual situation of the active distribution network. The dimensions of the position correspond to the initial values ​​of the distributed power output, the charging and discharging power of the energy storage equipment, and the load adjustment.

[0083] Then, the cost and elasticity index are calculated. The operating cost of the active distribution network is calculated according to the particle position through methods such as power flow calculation, and the elasticity of the active distribution network is evaluated from multiple aspects. For example, when a fault occurs, the time to restore power supply is recorded, the voltage fluctuation range is detected, and the power shortage is calculated. The elasticity index is obtained by combining these factors.

[0084] Then update the particles, and update the speed and position of the particles according to the established formula, so that the particles can continuously explore better solutions in the search space.

[0085] Then the constraint conditions are verified, and strict limit checks are performed on the active distribution network voltage and distributed power capacity. If the node voltage exceeds the specified range or the distributed power output exceeds its capacity, the particle position is adjusted in time or a penalty is given to ensure the feasibility of the solution.

[0086] Finally, iterative optimization is performed and the above steps are repeated. After multiple iterations, the particle swarm gradually converges to the optimal solution. The combination of distributed power output, energy storage device charging and discharging power, and load adjustment corresponding to the particle position is the optimal supply and demand coordination solution. Therefore, this solution minimizes the operating cost of the active distribution network while also enabling the active distribution network to have optimal flexibility.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A supply-demand coordinated active distribution network elasticity optimization method, characterized in that: The steps include: S1, collects the output data of distributed power sources of the active distribution network, collects the charging and discharging power data of the energy storage equipment of the active distribution network, and collects the load adjustment data of the active distribution network, where the distributed power sources include photovoltaic power generation and wind power generation; S2, using the distributed power output data in the active distribution network, the charging and discharging power data of the energy storage equipment and the load adjustment data as particles in the particle swarm algorithm, and initializing the particles; S3, calculates the operating cost and elasticity index of the active distribution network based on the particle positions; S4, update the particle's velocity and position, where the velocity update formula is: In the above formula, is the velocity of the i-th particle in the k+1-th iteration in the d-th dimension; w is the inertia weight; is the speed of the i-th particle in the k-th iteration in the d-th dimension; c1 and c2 are learning factors, also known as acceleration constants, c1 is the step size for adjusting the particle to fly to its own optimal position, and c2 is the step size for adjusting the particle to fly to the global optimal position; r1 and r2 are random numbers uniformly distributed in the interval [0,1]; is the individual optimal position of the ith particle in the dth dimension up to the kth iteration; is the current position of the i-th particle at the k-th iteration in the d-th dimension; is the global optimal position of the entire particle swarm in the dth dimension up to the kth iteration; The position update formula is: In the above formula, is the position of the i-th particle after the k+1-th iteration in the d-th dimension; is the position of the i-th particle at the k-th iteration in the d-th dimension; is the velocity of the i-th particle in the k+1-th iteration in the d-th dimension; S5, verify whether the voltage of each node meets the grid voltage limit and whether the output of the distributed power generation meets the capacity limit. If not, make adjustments or give penalties; S6, repeat the above steps until the iteration stop condition is met, and the optimal supply and demand coordination solution is obtained, so that the active distribution network has the optimal elasticity while minimizing the operating cost.

2. The method for optimizing the elasticity of an active distribution network with coordinated supply and demand according to claim 1, characterized in that: The operating cost includes the distributed power generation cost, the energy storage equipment loss cost and the demand response cost, which is the sum of the three.

3. The method for optimizing the elasticity of an active distribution network with coordinated supply and demand according to claim 2, characterized in that: The distributed power generation cost includes the photovoltaic distributed power generation cost and the wind power distributed power generation cost, wherein the calculation formula of the photovoltaic distributed power generation cost is: C genpv =C pv ×P pv ; In the above formula, C pv is the photovoltaic equipment cost coefficient; P pv Provide power for photovoltaic power; The calculation formula for the cost of wind power distributed power generation is: C genw =C w ×P w ; In the above formula, C w is the cost coefficient of wind power equipment; P w Providing wind power.

4. The method for optimizing the elasticity of an active distribution network with coordinated supply and demand according to claim 2, characterized in that: The calculation formula of the energy storage equipment loss cost is: C losses =C es ×|P es |×n; In the above formula, C es is the loss cost coefficient of energy storage equipment; P es is the charging and discharging power of the energy storage device; n is the number of charging and discharging times in a certain period of time.

5. The method for optimizing the elasticity of an active distribution network with coordinated supply and demand according to claim 2, characterized in that: The calculation formula of the demand response cost is: C DR =C d ×|ΔP d |; In the above formula, C d The unit compensation price for load users; ΔP d The load adjustment amount for demand response.

6. The method for optimizing the elasticity of an active distribution network with coordinated supply and demand according to claim 1, characterized in that: The elasticity index includes power supply restoration time after a fault, voltage fluctuation range and power shortage factor, and the elasticity index is constructed by comprehensively considering the three factors.

7. The method for optimizing elasticity of active distribution network with coordinated supply and demand according to claim 6, characterized in that: The post-fault power supply recovery time refers to the time from the occurrence of the fault to the recovery of all non-fault areas to the normal power supply voltage range T r , where the normal supply voltage range is ±5% of the rated voltage.

8. The method for optimizing the elasticity of an active distribution network with coordinated supply and demand according to claim 6, characterized in that: The voltage fluctuation range refers to the voltage V of each node of the active distribution network. i With rated voltage V n The maximum absolute value of the deviation can be expressed as max(|V i -V n |), where the voltage of each node is V i It is obtained through power flow calculation under various operating conditions, and the operating conditions include normal operating conditions and post-fault recovery processes.

9. The method for optimizing elasticity of active distribution network with coordinated supply and demand according to claim 6, characterized in that: The power deficit refers to the total load demand P of the active distribution network. load The actual available power P avail The difference between the two can be expressed as ΔP = P load -P avail , where the actual available power P avail It is the sum of the power output of the distributed power source and the discharge power of the energy storage device determined according to the particle position.

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