CN110837912A - Energy storage system capacity planning method based on investment benefits - Google Patents
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Abstract
The invention provides a power distribution network energy storage system capacity planning method based on investment benefits, which comprises the following steps: collecting information of the power distribution network to determine a topological structure and load capacity of the power distribution network and create a mathematical model of energy storage planning of the power distribution network; optimizing an energy storage planning model of the power distribution network by adopting a particle swarm algorithm, and initializing a plurality of parameters of particles; judging whether each particle meets the load flow constraint and the node voltage constraint of the power distribution network, if so, calculating the fitness value of each particle, and initializing the local optimal value and the all optimal values; updating the speed and the position of the particles according to a position and speed updating formula of the particle swarm algorithm; and judging whether the updated particles meet the set load flow constraint and node voltage constraint again, if so, calculating the value of the objective function of the current planning scheme, and updating the local optimal value and the all optimal values.
Description
Technical Field
The invention relates to a power distribution network energy storage planning method considering investment decision, and belongs to the field of energy storage system capacity planning and analysis.
Background
In recent years, the rapid development of energy storage technology introduces a new idea for the operation of a power distribution network, namely, the energy storage system is built, the energy is discharged at the peak and charged at the valley, and the purposes of reducing load fluctuation and obtaining economic benefit can be achieved.
For power grid users, the construction of an energy storage system is a large project, energy storage investment decisions need to be made, and a planning method with practical significance is selected. The existing planning method is not started from the actual situation, the construction cost is mostly only the purchase cost and the use and maintenance cost of equipment, the income brought to the power grid by the stored energy is not considered, and the practical guidance significance is not strong; meanwhile, in an actual use scene and a longer use period, the battery is used as main equipment of the energy storage system, and the electrochemical characteristics of the battery determine that the battery is greatly influenced by the electrochemical life health degree in use, so that the risk brought by the use time of the battery is also considered in energy storage planning, and the energy storage system planning method with practical construction guiding significance is obtained.
Disclosure of Invention
The invention provides a power distribution network energy storage planning method considering energy storage investment decisions, which can link the income of a power grid and the investment cost of the power grid by considering the income brought by energy storage to obtain the earning rate of energy storage, and the energy storage investment decisions are made according to the earning rate of the energy storage. Has stronger practical and guiding significance. According to the power distribution network energy storage planning method, the cost and the profit of energy storage during energy storage site selection and volume fixing in the power distribution network are considered, the maximum investment profit is taken as an objective function, the energy storage system in the power distribution network is selected and volume fixing is carried out, the overall investment profit rate of the energy storage is increased, and the objective function is solved by adopting a particle swarm algorithm.
The purpose of the invention is realized by the following technical scheme: a capacity planning method for a power distribution network energy storage system based on investment benefits comprises the following steps:
step 1: collecting information of the power distribution network to determine a topological structure and load capacity of the power distribution network, and creating a mathematical model of power distribution network energy storage planning, wherein the mathematical model of the power distribution network energy storage planning is as follows:
maxC＝Cs+Closs-Cess-Cess，om(1)
in the formula, CsFor low storage and high release annual profit value, C of the energy storage systemlossSaving the annual profit value of electric energy loss for energy storage system, CessIs the annual investment cost value of the energy storage system, Cess，omFor annual operating maintenance cost values of the energy storage system, they are calculated as follows:
annual investment cost of energy storage system:
in the formula (2), Ee,j，CressCapacity and residual value of the jth energy storage system are respectively; k is a radical ofeInvestment cost value per unit volume; ceThe expression of the annual value coefficient of the capital of the energy storage system is as follows
Ce＝[r·(1+r)h]/[(1+r)h-1](3)
In the formula (3), r and h are respectively the discount rate and the service life of the BESS;
annual operating maintenance costs of the energy storage system:
the energy storage system has low storage and high release annual yield:
Cs＝ks,y·(Cs,dis-Cs,e) (5)
in formula (5), ks,yFor the conversion coefficient of the typical daily energy storage system arbitrage income into annual arbitrage income, 300 are taken out in the technical scheme; cs,dis，Cs,eThe specific calculation formula is as follows:
in the above formulae (6) and (7), mdis，mchRespectively discharging electricity price and charging electricity price for the energy storage system; pess,j,dis(t')，Pess,j,c(t) the discharging power of the energy storage system at the moment t' and the charging power of the energy storage system at the moment t are respectively;
the energy storage system saves the annual income of electric energy loss:
in the above formula (8), maFor electricity price, D Ploss(t) the active loss saved during unit operation of the energy storage system;
step 2: optimizing the energy storage planning model of the power distribution network by adopting a particle swarm algorithm, initializing the speed and position of particles, the particle dimension, the number M of the particles, a learning factor, an inertia weight, the maximum speed of the particles and the maximum iteration frequency, wherein the particle dimension is the number of load nodes in the power distribution network, and the value of the particles represents the capacity of an energy storage system at a node corresponding to the current particle;
and step 3: judging whether each particle meets the load flow constraint and the node voltage constraint of the power distribution network, if so, calculating the fitness value of each particle, namely the sum of all energy storage benefits of the energy storage system, and initializing the local optimal value and all optimal values; the load flow constraint calculation mode of the power distribution network is as follows:
in the above formula (9), PG,j、QG,jRespectively injecting active power and reactive power at a node j; pL,j、QL,jThe active power and the reactive power of the load at the node j are obtained; gj,k、Bj,kThe conductance and susceptance values of the power grid line are obtained; u shapejIs the voltage at node j, UkIs the voltage at node k, δj,kIs the phase angle between nodes j, k; the node voltage constraint of the power distribution network satisfies:
Umin,j≤Uj≤Umax,j(10)
in the above formula (10), Umax,jFor the maximum voltage value, U, allowed during operation of the nodemin,jThe minimum voltage value allowed by the node operation is obtained;
and 4, step 4: updating the speed and the position of the particle according to a position and speed updating formula of the particle swarm algorithm, wherein the position and speed updating formula of the particle swarm algorithm is as follows:
vij(t+1)＝vij(t)+c1r1(t)[pij(t)-xij(t)]+c2r2(t)[pgj(t)-xij(t)]
(11)
xij(t+1)＝xij(t)+vij(t+1)
in the above formula (11), pgjThe best position the particle has experienced during flight; p is a radical ofijSearching an optimal position for each particle; i is 1,2, …, N, N is the number of particles; d is 1,2, …, and D is the dimension of the target search space; c. C1And c2Is an acceleration factor; r is1And r2Is a random number between [0, 1 ]]To (c) to (d); v. ofij(t) and xij(t) is the velocity and position of the particle in the tth iteration;
and 5: judging whether the updated particles meet the power flow constraint set by the formula (9) and the node voltage constraint set by the formula (10) again, if so, calculating the value of the objective function of the current planning scheme, and updating the local optimal value and the total optimal value;
step 6: if the maximum update iteration times is reached, finishing the calculation and outputting a result; otherwise, turning to the step 4 to repeat.
The invention provides a power distribution network energy storage planning method considering energy storage investment decisions, which considers the income brought by energy storage to a power grid, can link the income with the investment cost, makes the energy storage investment decisions according to the income rate of energy storage, has stronger practice guidance significance and is convenient to popularize.
Drawings
Fig. 1 is a schematic step diagram of an embodiment of a power distribution network energy storage planning method according to the present invention, in which a particle swarm optimization algorithm is used.
Detailed Description
The purpose of the invention is realized by the following technical scheme: a capacity planning method for a power distribution network energy storage system based on investment benefits comprises the following steps:
step 1: collecting information of the power distribution network to determine a topological structure and load capacity of the power distribution network, and creating a mathematical model of power distribution network energy storage planning, wherein the mathematical model of the power distribution network energy storage planning is as follows:
maxC＝Cs+Closs-Cess-Cess，om(1)
in the formula, CsFor low storage and high release annual profit value, C of the energy storage systemlossSaving the annual profit value of electric energy loss for energy storage system, CessIs the annual investment cost value of the energy storage system, Cess，omFor annual operating maintenance cost values of the energy storage system, they are calculated as follows:
annual investment cost of energy storage system:
in the formula (2), Ee,j，CressCapacity and residual value of the jth energy storage system are respectively; k is a radical ofeInvestment cost value per unit volume; ceThe expression of the annual value coefficient of the capital of the energy storage system is as follows
Ce＝[r·(1+r)h]/[(1+r)h-1](3)
In the formula (3), r and h are respectively the discount rate and the service life of the BESS;
annual operating maintenance costs of the energy storage system:
the energy storage system has low storage and high release annual yield:
Cs＝ks,y·(Cs,dis-Cs,e) (5)
in formula (5), ks,yFor the conversion coefficient of the typical daily energy storage system arbitrage income into annual arbitrage income, 300 are taken out in the technical scheme; cs,dis，Cs,eThe specific calculation formula is as follows:
in the above formulae (6) and (7), mdis，mchRespectively discharging electricity price and charging electricity price for the energy storage system; pess,j,dis(t')，Pess,j,c(t) the discharging power of the energy storage system at the moment t' and the charging power of the energy storage system at the moment t are respectively;
the energy storage system saves the annual income of electric energy loss:
in the above formula (8), maFor electricity price, D Ploss(t) the active loss saved during unit operation of the energy storage system;
step 2: and optimizing the energy storage planning model of the power distribution network by adopting a particle swarm algorithm, and initializing the speed and the position of particles, the particle dimension, the number M of the particles, a learning factor, an inertia weight, the maximum speed of the particles and the maximum iteration times.
The particle dimension is the number of load nodes in the power distribution network, and the value of the particle represents the capacity of the energy storage system at the node corresponding to the current particle;
and step 3: judging whether each particle meets the load flow constraint and the node voltage constraint of the power distribution network, if so, calculating the fitness value of each particle, namely the sum of all energy storage benefits of the energy storage system, and initializing the local optimal value and all optimal values; the load flow constraint calculation mode of the power distribution network is as follows:
in the above formula (9), PG,j、QG,jRespectively injecting active power and reactive power at a node j; pL,j、QL,jThe active power and the reactive power of the load at the node j are obtained; gj,k、Bj,kThe conductance and susceptance values of the power grid line are obtained; u shapejIs the voltage at node j, UkIs the voltage at node k, δj,kIs the phase angle between nodes j, k; the node voltage constraint of the power distribution network satisfies:
Umin,j≤Uj≤Umax,j(10)
in the above formula (10), Umax,jFor the maximum voltage value, U, allowed during operation of the nodemin,jThe minimum voltage value allowed by the node operation is obtained;
and 4, step 4: updating the speed and the position of the particle according to a position and speed updating formula of the particle swarm algorithm, wherein the position and speed updating formula of the particle swarm algorithm is as follows:
vij(t+1)＝vij(t)+c1r1(t)[pij(t)-xij(t)]+c2r2(t)[pgj(t)-xij(t)]
(11)
xij(t+1)＝xij(t)+vij(t+1)
in the above formula (11), pgjThe best position the particle has experienced during flight; p is a radical ofijSearching an optimal position for each particle; i is 1,2, …, N, N is the number of particles; d is 1,2, …, D is target search spaceDimension; c. C1And c2Is an acceleration factor; r is1And r2Is a random number between [0, 1 ]]To (c) to (d); v. ofij(t) and xij(t) is the velocity and position of the particle in the tth iteration;
and 5: judging whether the updated particles meet the network connectivity constraint of the power distribution network, the power flow constraint set according to the formula (9) and the node voltage constraint set according to the formula (10) again, calculating the fitness value of the updated particles if the updated particles meet the constraints, and updating the local optimal value and the total optimal value;
step 6: if the maximum update iteration times is reached, finishing the calculation and outputting a result; otherwise, turning to the step 4 to repeat.
Claims (1)
1. A power distribution network energy storage system capacity planning method based on investment benefits is characterized by comprising the following steps:
step 1: collecting information of the power distribution network to determine a topological structure and load capacity of the power distribution network, and creating a mathematical model of power distribution network energy storage planning, wherein the mathematical model of the power distribution network energy storage planning is as follows:
maxC＝Cs+Closs-Cess-Cess，om(1)
in the formula, CsFor low storage and high release annual profit value, C of the energy storage systemlossSaving the annual profit value of electric energy loss for energy storage system, CessIs the annual investment cost value of the energy storage system, Cess，omFor annual operating maintenance cost values of the energy storage system, they are calculated as follows:
annual investment cost of energy storage system:
in the formula (2), Ee,j，CressCapacity and residual value of the jth energy storage system are respectively; k is a radical ofeInvestment cost value per unit volume; ceThe expression of the annual value coefficient of the capital of the energy storage system is as follows
Ce＝[r·(1+r)h]/[(1+r)h-1](3)
In the formula (3), r and h are respectively the discount rate and the service life of the BESS;
annual operating maintenance costs of the energy storage system:
the energy storage system has low storage and high release annual yield:
Cs＝ks,y·(Cs,dis-Cs,e) (5)
in formula (5), ks,yFor the conversion coefficient of the typical daily energy storage system arbitrage income into annual arbitrage income, 300 are taken out in the technical scheme; cs,dis，Cs,eThe specific calculation formula is as follows:
in the above formulae (6) and (7), mdis，mchRespectively discharging electricity price and charging electricity price for the energy storage system; pess,j,dis(t')，Pess,j,c(t) the discharging power of the energy storage system at the moment t' and the charging power of the energy storage system at the moment t are respectively;
the energy storage system saves the annual income of electric energy loss:
in the above formula (8), maFor electricity price, D Ploss(t) the active loss saved during unit operation of the energy storage system;
step 2: optimizing the energy storage planning model of the power distribution network by adopting a particle swarm algorithm, initializing the speed and position of particles, the particle dimension, the number M of the particles, a learning factor, an inertia weight, the maximum speed of the particles and the maximum iteration frequency, wherein the particle dimension is the number of load nodes in the power distribution network, and the value of the particles represents the capacity of an energy storage system at a node corresponding to the current particle;
and step 3: judging whether each particle meets the load flow constraint and the node voltage constraint of the power distribution network, if so, calculating the fitness value of each particle, namely the sum of all energy storage benefits of the energy storage system, and initializing the local optimal value and all optimal values; the load flow constraint calculation mode of the power distribution network is as follows:
in the above formula (9), PG,j、QG,jRespectively injecting active power and reactive power at a node j; pL,j、QL,jThe active power and the reactive power of the load at the node j are obtained; gj,k、Bj,kThe conductance and susceptance values of the power grid line are obtained; u shapejIs the voltage at node j, UkIs the voltage at node k, δj,kIs the phase angle between nodes j, k; the node voltage constraint of the power distribution network satisfies:
Umin,j≤Uj≤Umax,j(10)
in the above formula (10), Umax,jFor the maximum voltage value, U, allowed during operation of the nodemin,jThe minimum voltage value allowed by the node operation is obtained;
and 4, step 4: updating the speed and the position of the particle according to a position and speed updating formula of the particle swarm algorithm, wherein the position and speed updating formula of the particle swarm algorithm is as follows:
vij(t+1)＝vij(t)+c1r1(t)[pij(t)-xij(t)]+c2r2(t)[pgj(t)-xij(t)]
(11)
xij(t+1)＝xij(t)+vij(t+1)
in the above-mentioned formula (11),pgjthe best position the particle has experienced during flight; p is a radical ofijSearching an optimal position for each particle; i is 1,2, …, N, N is the number of particles; d is 1,2, …, and D is the dimension of the target search space; c. C1And c2Is an acceleration factor; r is1And r2Is a random number between [0, 1 ]]To (c) to (d); v. ofij(t) and xij(t) is the velocity and position of the particle in the tth iteration;
and 5: judging whether the updated particles meet the power flow constraint set by the formula (9) and the node voltage constraint set by the formula (10) again, if so, calculating the value of the objective function of the current planning scheme, and updating the local optimal value and the total optimal value; and
step 6: if the maximum update iteration times is reached, finishing the calculation and outputting a result; otherwise, turning to the step 4 to repeat.
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| CN117993740A (en) * | 2024-04-03 | 2024-05-07 | 国网山西省电力公司营销服务中心 | Multi-element power distribution network configuration method considering N-1 fault load loss cost |
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