Optimization method for hybrid energy storage system of power distribution network

Abstract

The invention, which belongs to the technical field of the power distribution network, discloses an optimization method for a hybrid energy storage system of a power distribution network, comprising the following steps: step 1, constructing a flexible grid-connected model of a photovoltaic-hybrid energy storage system; 2, constructing a double-layer random model for improving the reliability of the power distribution network; 3, setting an optimized operation process of the power distribution network; and 4, setting a control mode. According to the method, the economic advantages of the HESS are developed to the greatest extent through coordinated control of the energy storage units of the hybrid energy storage system, the charge and discharge power of the HESS is controlled through an artificial neural network algorithm, the deviation between the wind power prediction power and the actual power is compensated, the state of charge (SOC) of the HESS is considered, overcharge or deep discharge of the ESS is caused, and the service life is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution networks, and particularly relates to an optimization method for a hybrid energy storage system of a distribution network. Background Art

[0002] Under the dual pressures of energy and environment, clean energy generation represented by photovoltaic has seen unprecedented development. However, high-penetration distributed photovoltaic power generation is prone to problems such as distribution network voltage over-limit when connected to the grid. With the increasing utilization rate of cables in urban distribution networks in China, the line impedance is relatively large, the voltage is sensitive to the change of active power, and both active / reactive power flows will have a greater impact on the node voltage, exacerbating the high-voltage problem of photovoltaic high-penetration distribution networks. Energy storage systems can suppress the power fluctuations of clean energy, perform peak shaving and valley filling, and conduct dispatching and tracking, and also provide new control means for the optimal operation of distribution networks. On the premise of meeting the full consumption of photovoltaic energy, the integrated photovoltaic and energy storage system can obtain the optimal grid-connected power for the economic operation of the distribution network by reasonably adjusting the energy charge and discharge of the energy storage; according to the reactive power output of the remaining capacity of the inverter, through the comprehensive regulation of active / reactive power, the node voltage of the whole network can be effectively controlled. This flexible grid-connection method of the photovoltaic and energy storage system strengthens the support and regulation functions for the power grid. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and provide an optimization method for a hybrid energy storage system of a distribution network, which solves the problems in the above background art.

[0004] The purpose of the present invention is achieved as follows: An optimization method for a hybrid energy storage system of a distribution network, which includes the following steps:

[0005] Step 1: Construct a flexible grid-connection model for a photovoltaic-hybrid energy storage system;

[0006] Step 2: Construct a two-layer stochastic model for improving the reliability of the distribution network;

[0007] Step 3: Set the optimization operation process of the distribution network;

[0008] Step 4: Set the control mode.

[0009] Further, in Step 1, the battery and the capacitor are connected in parallel through their respective DC / DC converters to form a hybrid energy storage system. After converging with the photovoltaic system to a common DC bus, it is then connected to the distribution system through a DC / AC inverter; a shared AC / DC grid-connected converter is used to uniformly control the optimal reactive power Q total (t) for realizing the economic operation of the power source-network of the distribution network and the treatment of node voltage over-limit on the premise of full consumption of photovoltaic energy; the grid-connected active power P total (t) is the photovoltaic P PV (t), the hybrid energy storage P hess(t) and DC / AC conversion efficiency η dc-ac is a function of

[0010] P hess (t) = η dc-ac [P PV (t) + P hess (t)]

[0011] The grid-connected reactive power Q total (t) is generated by the remaining capacity of the AC / DC inverter, and the output range must satisfy the inverter capacity and the active grid-connected power constraint as

[0012]

[0013] where S inverter is the rated capacity of the AC / DC converter;

[0014] The photovoltaic unit adopts the MPPT control strategy, combined with the power regulation of the common DC bus energy storage, to ensure the full-capacity and friendly consumption of photovoltaic clean energy. η dc-dc is the two-stage photovoltaic DC / DC conversion efficiency;

[0015] P PV (t) = η dc-dc P MPPT (t)

[0016] P hess (t) is the sum of the battery P bat (t) and the supercapacitor P cap (t);

[0017] P hess (t) = P bat (t) + P cap (t)

[0018] Both the battery and the supercapacitor include charging, idle, and discharging states. Taking the discharging power as positive, the expressions for considering the loss power are respectively:

[0019]

[0020] where P bat，c (t), P bat，d (t), P cap，c (t), P cap，d (t), η bat，c , η bat，d , η cap，c , η cap，d are respectively the charge / discharge power, DC / DC conversion efficiency, and energy conversion efficiency of the energy storage body of the battery and the supercapacitor;

[0021] To avoid overcharging and deep discharging, calculations are performed for P bat (t) and P cap (t). Substitute their output ranges into the equation to determine the output range of P total (t);

[0022]

[0023] Equations (1) and (2) above represent the charging process. P bat，clim and P cap，clim are the charging power limits of the battery and the supercapacitor during the t period, respectively; C bat , C cap , P bat，cmax , P cap，cmax are the rated capacity and the maximum charging power of the battery and the supercapacitor, respectively; SOC bat，max and SOC cap，max are the upper limit values of the state of charge of the battery and the supercapacitor, respectively; Δt is the duration of each calculation period;

[0024] Equations (3) and (4) above represent the discharging process. P bat，dlim and P cap，dlim are the discharging power limits of the battery and the supercapacitor during the t period, respectively; P bat，dmax and P cap，dmax are the maximum discharging power of the battery and the supercapacitor, respectively; SOC bat，min and SOC cap，min are the lower limit values of the state of charge of the battery and the supercapacitor, respectively.

[0025] Furthermore, Step 2 includes an upper-layer expansion planning model and a lower-layer distribution network reliability model;

[0026] The objective function of the upper-layer expansion planning model aims to minimize the present value of the total investment and operating cost of the system within the planning period. The objective function consists of three parts. The first term of the function represents the current value of the investment cost under the assumption of a permanent or infinite planning horizon. When the equipment life expires, reinvestment in the same equipment is required. The second term of the objective function is the present value of the operating cost within the planning period, and the third term of the objective function is the present value of the operating cost occurring after the planning time stage;

[0027]

[0028] Among them, E S is the present value of the total system cost; represents the total investment cost in the t-th year, which consists of the investment cost of the line, the investment cost of the wind turbine, the investment cost of PV, the construction cost of the substation, the substation expansion cost, and the investment cost of ESS; is the annual total operating cost of the substation, is the total power purchase cost of the system in the t-th year, including the power purchase cost from the superior power grid through the substation, the power purchase cost from renewable energy and ESS. is the annual network loss cost; I f represents the interest rate;

[0029] The annual investment cost of the power grid is calculated as follows.

[0030]

[0031] Among them, represents the unit construction cost of the line, l ij is the length of the distribution network line ij, z ij is the 0-1 investment decision variable of the line ij, where ij belongs to the set of alternative lines Ω of the distribution network CL ; is the 0-1 investment decision variable of the wind turbine and PV. and are the construction costs of the wind turbine and PV; is the 0-1 investment decision variable related to the construction and expansion of the substation. is the construction cost of the substation investment and capacity expansion, Ω SUB is the set of nodes including existing substations and alternative substations; is the 0-1 decision variable of the construction investment of ESS. is the construction cost of ESS, Ω ESS is the set of alternative nodes of ESS; f DL , f W , f PV , f SUB , f RE and f ESS are the asset depreciation rates of various types of assets;

[0032] The operation equation of the substation is as follows.

[0033]

[0034] Among them, b ∈ Ω B is the set of scenarios of the annual typical load level. and Δb represent the total annual hours and the simulated operation unit duration of the typical scenario b; ξ e,b represents the probability of the uncertain scenario e under the load level b; is the unit operation cost of the substation; represents the square of the apparent power in the random scenario e when the load level of the substation in the t-th year is b;

[0035] Substation capacity constraint;

[0036] The active and reactive power outputs of the substation are limited by the maximum active and reactive power outputs. As shown in the above formula, the maximum active and reactive power output limits of the substation can be determined by the above formula. The above formula limits the square of the apparent power of the substation to be less than the square of its maximum capacity. The maximum capacity of the substation depends on two parts: the initial installed capacity and the capacity of upgrade and expansion.

[0037]

[0038] Among them, P Gmax and Q Gmax represent the maximum active and reactive power outputs of the substation respectively; S Gmax and S Gre represent the installed capacity and the expansion capacity of the substation; α G is the pre-set maximum power factor allowed for the substation, and α G ∈[0,1].

[0039] Furthermore, the reliability model of the lower-level distribution network includes a lower-level objective function. The lower-level scheduling strategy of the lower-level objective function can be expressed as minimizing the penalty cost of EENS under the condition of random fault operation. The formula is

[0040]

[0041] Among them, C EENS,t represents the annual total cost of EENS, and E EENS is the present value of the total cost of EENS; c voll represents the unit load loss penalty unit price; ρ k represents the probability that the system operates under the accidental random fault event k; P VOLL,k,i,t,b represents the load loss amount of node i under the accidental event scenario k;

[0042] Operating constraints restricted by the upper-level decision; Since the investment strategy has been determined by the upper-level model, the distribution network can only follow the construction layout plan determined by the upper-level during the fault operation mode. The output characteristic constraints of renewable generating units and ESSs under the accidental event k, whether the substation operates normally after the fault event k occurs. When the substation fails, the apparent load power of the substation is constantly 0, and the maximum load loss value among all faults is screened and passed to the upper-level planning model;

[0043]

[0044] Among them, and represent the charging and discharging powers of ESS at the fault event k respectively, and they must satisfy the relevant constraints of ESS operation; It is the real-time operating capacity of the ESS when fault k occurs. The symbol * shown in the formula represents the decision variable in the upper-layer planning model, which is transmitted to the lower layer and converted into fixed parameters.

[0045] Furthermore, Step 3 includes setting the optimization operation cycle to 24 hours, with 1 minute as the optimization time period. Each optimization uses the internal parameter values of the PHESS at the initial moment and the real-time power flow data of the distribution network provided by the distribution network automation system. Considering the uncertainty of photovoltaic power output, the PHESS optimizes the distribution network operation strategy based on two models: the source-network economic operation and the grid-connected node voltage over-limit control of the distribution network operation. The optimization operation primarily focuses on safety issues, and the priority of grid-connected node voltage over-limit control in the strategy is higher than that of economic operation.

[0046] The source-network economic operation model takes the minimum source-network loss as the objective function. The source-network loss consists of the distribution network line loss P L,loss (t) and the operation loss of P PHESS,loss (t); P L，loss (t) represents the sum of the active power injection powers of each node in the system; P PHESS,loss (t) includes the photovoltaic DC / DC loss, the energy storage operation loss, and the grid-connected AC / DC loss. The objective function is as follows,

[0047] F = min[P L，loss (t) + P PHESS,loss (t)]

[0048]

[0049] P PHESS，loss (t) = (1 - η dc-c )P MPPT (t) + (1 - η bat，c )P bat，c (t) +

[0050] (1 - η bat，d )P bat，d (t) + (1 - 1η cap,c )P cap，c (t) +

[0051] (1 - η cap，d )P cap，d (t) + (t)ΔP s-loss +

[0052] (1 - η dc-ac )P MPPT [P PV (t) + P hess (t)]

[0053] In the formula, N is the number of system nodes; P i(t) is the active power injected by node i at time t; ξ(t) is the dimension variable of the charge and discharge state change of the battery, and ξ(t) is 0 or 1; ΔP s-loss is the loss generated when the charge and discharge state of the energy storage switches at time t, and is counted as a constant;

[0054] The constraint conditions include system power flow constraints and substation outlet power constraints;

[0055] The system power flow constraint is,

[0056]

[0057] In the formula, i and j are system node numbers; U i (t), U j (t) are the voltage amplitudes of nodes i and j at time t; G ij , B ij are the mutual conductance and mutual susceptance between nodes i and j respectively; δ ij (t) is the phase difference between nodes i and j at time t; P K,i (t), Q K,i (t), P D,i (t), Q D,i (t), P total，t (t), Q total，t (t), P PV,i , P WT,i are the active and reactive powers at the substation outlet, load active and reactive powers, PHESS active and reactive powers, and the grid-connected powers of distributed PV and wind power systems at node i at time t respectively;

[0058] The substation outlet power constraint is,

[0059]

[0060] In the formula, P K,max , P K,min , Q K,max , Q K,min are the upper and lower limits of the active and reactive powers at the substation outlet respectively.

[0061] Further, the fourth step includes classifying the operating states of the HESS according to the SOC states and charge-discharge states of the battery and the supercapacitor. Different operating states adopt different types of HESS control strategies, including multi-objective frequency division control, internal energy coordination control, and stop operation. When the SOCs of both the battery and the supercapacitor are in normal states, the HESS adopts the multi-objective frequency division control strategy to give full play to the complementary advantages of the battery and supercapacitor technologies and extend the operating life of the HESS. When only one of the SOCs of the battery and the supercapacitor meets the single-operation condition, the HESS adopts the internal energy coordination control strategy to ensure that the battery and the supercapacitor operate within their respective safe ranges. If the SOCs of the battery and the supercapacitor do not meet the operating conditions, the HESS stops the optimized operation and charges and discharges the HESS to quickly return to the normal working state.

[0062] Further, the upper and lower limits of the SOCs of the battery and the supercapacitor are [0.85, 0.15] and [0.95, 0.05] respectively. The specific steps are as follows.

[0063] First, adopt the PHESS to participate in the distribution network optimization operation strategy, calculate the output power P hess (t) of the HESS, and judge the operating state of the HESS according to P hess (t).

[0064] Second, according to the HESS state classification and combining the SOCs of the battery and the supercapacitor at time t, determine the corresponding control strategy of the HESS.

[0065] Third, according to the different control strategies of the HESS determined in the second step, calculate the charge-discharge powers of the battery and the supercapacitor respectively.

[0066] The beneficial effects of the present invention: Through the coordinated control of each energy storage unit of the hybrid energy storage system, the economic advantages of the HESS are maximally exerted. The charge-discharge power of the HESS is controlled by the artificial neural network algorithm to compensate for the deviation between the predicted wind power and the actual power. Considering its state of charge (SOC), overcharging or deep discharging of the ESS is avoided, and the operating life is extended.

[0067] The wavelet algorithm is used to decompose the output power of the wind farm to obtain the high-frequency fluctuation power and the low-frequency fluctuation power; through the SOC optimization control, the high- and low-frequency fluctuation powers are respectively used as the charge and discharge reference powers of the supercapacitor and the battery. The fuzzy control theory is adopted to distribute the output powers of the battery and the supercapacitor to extend the operation life of the battery; the supercapacitor follows the principle of prior response to suppress the wind power fluctuation, considering optimizing the control of the SOC of the supercapacitor, through the spectrum analysis of the photovoltaic power signal, the wavelet packet decomposition is used to determine the charge and discharge powers of the energy-type and power-type energy storages, and the fuzzy control is used to optimize the SOC of the power-type energy storage, so that the power-type energy storage can better meet the charge and discharge requirements at the next moment and improve the suppression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is the operation flowchart of the distribution network of the present invention;

[0069] Figure 2 is the implementation flowchart of the double-layer programming model of the present invention;

[0070] Figure 3 is the control flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0071] The present invention will be further described in detail below with reference to the drawings. It should be noted that the purpose is only to illustrate and explain the present invention more clearly.

[0072] As Figures 1-3 shown, this embodiment discloses an optimization method for a distribution network hybrid energy storage system, which includes the following steps:

[0073] Step 1: Construct a flexible grid-connected model for a photovoltaic-hybrid energy storage system;

[0074] The battery and the capacitor are connected in parallel through their respective DC / DC converters to form a hybrid energy storage system. After converging with the photovoltaic system to a common DC bus, it is then connected to the distribution system through a DC / AC inverter; a shared AC / DC grid-connected converter is used to uniformly control the optimal reactive power Q total (t) for realizing the economic operation of the power source-network and the governance of node voltage over-limit on the premise of full consumption of photovoltaic energy; the grid-connected active power P total (t) is a function of the photovoltaic P PV (t), the hybrid energy storage P hess (t) and the DC / AC conversion efficiency η dc-ac ;

[0075] P hess (t) = η dc-ac [P PV (t) + P hess (t)]

[0076] Grid-connected reactive power Q total (t) is generated by the remaining capacity of the AC / DC inverter, and the output range must satisfy the inverter capacity and the active grid-connected power constraint as

[0077]

[0078] In the formula, S inverter is the rated capacity of the AC / DC converter;

[0079] The photovoltaic unit adopts the MPPT control strategy, combined with the power regulation of the common DC bus energy storage, to ensure the full and friendly consumption of photovoltaic clean energy, η dc-dc is the two-stage photovoltaic DC / DC conversion efficiency;

[0080] P PV (t) = η dc-dc P MPPT (t)

[0081] P hess (t) is the sum of P bat (t) of the battery and P cap (t) of the supercapacitor;

[0082] P hess (t) = P bat (t) + P cap (t)

[0083] Both the battery and the supercapacitor include charging, idle, and discharging states. Taking the discharging power as positive, the expressions for considering the loss power are respectively:

[0084]

[0085] In the formula, P bat，c (t), P bat，d (t), P cap，c (t), P cap，d (t), η bat，c , η bat，d , η cap，c , η cap，d are respectively the charge / discharge power, DC / DC conversion efficiency, and energy conversion efficiency of the energy storage body of the battery and the supercapacitor;

[0086] To avoid overcharging and deep discharging, calculations are carried out on the output ranges of P bat (t) and P cap (t), and substituting them to determine the output range of P total (t);

[0087]

[0088]

[0089] The above formulas (1) and (2) are for the charging process. P bat，clim and P cap，clim are the charging power limits of the battery and the supercapacitor during the t period respectively; C bat and C cap and P bat，cmax and P cap，cmax are the rated capacity, maximum charging power of the battery and the supercapacitor respectively; SOC bat，max and SOC cap，max are the upper limit values of the state of charge of the battery and the supercapacitor respectively; Δt is the duration of each calculation period;

[0090] The above formulas (3) and (4) are for the discharging process. P bat，dlim and P cap，dlim are the discharging power limits of the battery and the supercapacitor during the t period respectively; P bat，dmax and P cap，dmax are the maximum discharging powers of the battery and the supercapacitor respectively; SOC bat，min and SOC cap，min are the lower limit values of the state of charge of the battery and the supercapacitor respectively.

[0091] Step 2: Construct a two - layer stochastic model for improving the reliability of the distribution network;

[0092] The two - layer stochastic model includes an upper - layer expansion planning model and a lower - layer distribution network reliability model;

[0093] The objective function of the upper - layer expansion planning model aims to minimize the present value of the total investment and operation cost of the system within the planning period. The objective function consists of three parts. The first term of this function represents the current value of the investment cost under the assumption of a permanent or infinite planning horizon. When the equipment life expires, reinvestment in the same equipment is required. The second term of the objective function is the present value of the operation cost within the planning horizon, and the third term of the objective function is the present value of the operation cost occurring after the planning time stage;

[0094]

[0095] Among them, E S is the present value of the total system cost; represents the total investment cost in the t - th year, which consists of the investment cost of the line, the investment cost of the wind turbine, the investment cost of PV, the substation construction cost, the substation expansion cost, and the ESS investment cost; is the annual total operation cost of the substation, is the total power purchase cost of the system in the t - th year, including the power purchase cost from the superior grid through the substation, the power purchase cost from renewable energy and ESS, is the annual network loss cost; I f represents the interest rate;

[0096] The annual investment cost of the power grid is calculated as follows:

[0097]

[0098] where, represents the unit construction cost of the line, l ij is the length of the distribution network line ij, z ij is the 0-1 investment decision variable of the line ij, and ij belongs to the set of alternative lines Ω of the distribution network CL ; is the 0-1 investment decision variable of the wind turbine and PV, and are the construction costs of the wind turbine and PV; is the 0-1 investment decision variable related to the construction and expansion of the substation, is the construction cost of the substation investment and expansion, Ω SUB is the set of nodes including existing substations and alternative substations; is the 0-1 decision variable of the construction investment of the ESS, is the construction cost of the ESS, Ω ESS is the set of alternative nodes of the ESS; f DL , f W , f PV , f SUB , f RE and f ESS are the asset depreciation rates of various types of assets;

[0099] The operation cost formula of the substation is,

[0100]

[0101] where, b ∈ Ω B is the set of scenarios of the annual typical load level, and Δb represent the total annual hours and the simulated operation unit duration of the typical scenario b; ξ e,b represents the probability of the uncertain scenario e under the load level b; is the unit operation cost of the substation; represents the square of the apparent power of the substation in the random scenario e with the load level b in the t-th year;

[0102] Substation capacity constraint;

[0103] The active and reactive power outputs of the substation are limited by the maximum active and reactive power outputs. As shown in the above formula, the maximum active and reactive power output limits of the substation can be determined by the above formula. The above formula limits the square of the apparent power of the substation to be less than the square of its maximum capacity. The maximum capacity of the substation depends on two parts: the initial installed capacity and the capacity of upgrade and expansion.

[0104]

[0105] Among them, P Gmax and Q Gmax respectively represent the maximum active and reactive power outputs of the substation; S Gmax and S Gre represent the installed capacity and expansion capacity of the substation; α G is the pre-set maximum power factor allowed for the substation, and α G ∈[0,1].

[0106] The lower-level distribution network reliability model includes a lower-level objective function. The lower-level scheduling strategy of the lower-level objective function can be expressed as minimizing the penalty cost of EENS under the random fault operating conditions. The formula is

[0107]

[0108] Among them, C EENS,t represents the annual total cost of EENS, and E EENS is the present value of the total cost of EENS; c voll represents the unit load loss penalty unit price; ρ k represents the probability that the system operates under the accidental random fault event k; P VOLL,k,i,t,b represents the load loss amount of node i under the accidental event scenario k;

[0109] Operating constraints restricted by the upper-level decision; Since the investment strategy has been determined by the upper-level model, the distribution network can only follow the construction layout plan determined by the upper-level under the fault operating mode. The output characteristic constraints of renewable generating units and ESSs under the accidental event k, whether the substation operates normally after the fault event k occurs. When the substation fails, the apparent load power of the substation is constantly 0, and the maximum load loss value among all faults is screened and transmitted to the upper-level planning model;

[0110]

[0111] Among them, and respectively represent the charging and discharging power of ESS at the fault event k, and they must satisfy the relevant operating constraints of ESS; It is the real-time operating capacity of the ESS when fault k occurs. The symbol * shown in the formula represents a decision variable in the upper-layer planning model, which is transmitted to the lower layer and converted into a fixed parameter.

[0112] Step 3: Set the optimal operation process of the distribution network;

[0113] The optimal operation process of the distribution network includes setting the optimal operation cycle to 24 hours, with 1 minute as the optimization time period. Each optimization uses the internal parameter values of the PHESS at the initial moment and the real-time power flow data of the distribution network provided by the distribution network automation system. Considering the uncertainty of photovoltaic power output, the PHESS optimizes the operation strategy of the distribution network based on two models: the source-network economic operation and the governance of grid-connected node voltage over-limit of the distribution network operation. The optimization operation primarily focuses on safety issues, and the priority of node voltage over-limit governance in the strategy is higher than that of economic operation;

[0114] The source-network economic operation model takes the minimum source-network loss as the objective function. The source-network loss consists of the distribution network line loss P L,loss (t) and P PHESS,loss (t) operating losses; P L , loss (t) represents the sum of the active power injection powers of each node in the system; P PHESS,loss (t) includes the photovoltaic DC / DC loss, the ESS operating loss, and the grid-connected AC / DC loss. The objective function is as follows,

[0115] F = min[P L，loss (t) + P PHESS，loss (t)]

[0116]

[0117] P PHESS，loss (t) = (1 - η dc-dc )P MPPT (t) + (1 - η bat,c )P bat，c (t) +

[0118] (1 - η bat，d )P bat，d (t) + (1 - η cap,c )P cap，c (t) +

[0119] (1 - η cap，d )P cap，d (t) + ξ(t)ΔP s-loss +

[0120] (1 - η dc-ac )P MPPT [P PV (t) + P hess (t)]

[0121] where N is the number of system nodes; P i (t) is the active power injection at node i at time t; ξ(t) is the dimension variable of the charge and discharge state of the battery, and ξ(t) is 0 or 1; ΔP s-loss is the loss generated when the charge and discharge state of the energy storage switches at time t, which is counted as a constant;

[0122] The constraint conditions include system power flow constraints and substation outlet power constraints;

[0123] The system power flow constraint is that,

[0124]

[0125] where i and j are system node numbers; U i (t), U j (t) are the voltage amplitudes of nodes i and j at time t; G ij , B ij are the mutual conductance and mutual susceptance between nodes i and j respectively; δ ij (t) is the phase difference between nodes i and j at time t; P K,i (t), Q K,i (t), P D,i (t), Q D,i (t), P total，t (t), Q total，t (t), P PV,i , P WT,i are the active and reactive powers at the substation outlet, the active and reactive powers of the load, the active and reactive powers of the PHESS, and the grid-connected powers of the distributed PV and wind power systems at node i at time t respectively;

[0126] The substation outlet power constraint is that,

[0127]

[0128] where P K,max , P K,min , Q K,max , Q K,min are the upper and lower limits of the active and reactive powers at the substation outlet respectively.

[0129] Step Four: Set the control mode;

[0130] Classify the operating states of HESS according to the SOC states and charge / discharge states of the battery and the supercapacitor. Different operating states adopt different types of HESS control strategies, including multi-objective frequency division control, internal energy coordination control, and stop operation. When the SOCs of both the battery and the supercapacitor are in normal states, HESS adopts the multi-objective frequency division control strategy to give full play to the complementary advantages of the battery and supercapacitor technologies and extend the operating life of HESS. When only one of the SOCs of the battery and the supercapacitor meets the single-operation condition, HESS adopts the internal energy coordination control strategy to ensure that the battery and the supercapacitor operate within their respective safe ranges. If the SOCs of the battery and the supercapacitor do not meet the operating conditions, HESS stops the optimized operation and charges / discharges HESS to quickly return to the normal working state. The upper and lower limits of the SOCs of the battery and the supercapacitor are [0.85, 0.15] and [0.95, 0.05] respectively.

[0131] The specific steps are as follows:

[0132] First, adopt the PHESS to participate in the distribution network optimization operation strategy, calculate the output power P hess (t) of HESS, and judge the operating state of HESS according to P hess (t).

[0133] Second, according to the HESS state classification and combined with the SOCs of the battery and the supercapacitor at time t, determine the corresponding control strategy of HESS.

[0134] Third, according to the different control strategies of HESS determined in the second step, calculate the charge / discharge powers of the battery and the supercapacitor respectively.

[0135] Use the wavelet algorithm to decompose the output power of the wind farm to obtain the high-frequency fluctuation power and the low-frequency fluctuation power; through SOC optimization control, use the high- and low-frequency fluctuation powers as the charge / discharge reference powers of the supercapacitor and the battery respectively. Use the fuzzy control theory to distribute the output powers of the battery and the supercapacitor to extend the operating life of the battery; the supercapacitor follows the principle of priority response to suppress the wind power fluctuation. Consider optimizing the control of the SOC of the supercapacitor. Through the spectrum analysis of the photovoltaic power signal, use wavelet packet decomposition to determine the charge / discharge powers of the energy-type and power-type energy storages, and use fuzzy control to optimize the SOC of the power-type energy storage to make the power-type energy storage better meet the charge / discharge requirements at the next moment and improve the suppression effect.

[0136] Through the coordinated control of each energy storage unit of the hybrid energy storage system, the economic advantages of the HESS are maximized. The charging and discharging power of the HESS is controlled by an artificial neural network algorithm to compensate for the deviation between the predicted wind power and the actual power. Considering its state of charge (SOC), overcharging or deep discharging of the ESS is avoided, and the operating life is extended.

[0137] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing a hybrid energy storage system in a distribution network, characterized in that: The following steps are involved: Step 1: Construct a flexible grid-connected model for the photovoltaic-hybrid energy storage system; Step 2: Construct a two-layer stochastic model for improving distribution network reliability; Step 3: Set the distribution network optimization operation process; Step 4: Set the control method.

2. The optimization method of the hybrid energy storage system of the distribution network according to claim 1, characterized in that: The step 1 includes connecting the battery and the capacitor in parallel through their respective DC / DC converters to form a hybrid energy storage system, merging with the photovoltaic system to a common DC bus, and then connecting to the power distribution system through a DC / AC inverter; a shared AC / DC grid-connected converter uniformly controls the optimal reactive power Q output of the grid-connected system. total (t), used to realize the source-grid economic operation of the distribution network and the control of node voltage over-limit under the premise of achieving full consumption of photovoltaic energy; grid-connected active power P total (t) is photovoltaic P PV (t), hybrid energy storage P hess (t) and DC / AC conversion efficiency η dc-ac The function of P hess (t)=η dc-ac [P PV (t)+P hess (t)] Grid-connected reactive power Q total (t) is generated through the remaining capacity of the AC / DC inverter. The output range must meet the inverter capacity and active grid-connected power constraints. In the formula, S inverter is the rated capacity of the AC / DC converter; The photovoltaic unit adopts MPPT control strategy, combined with the power regulation of common DC bus energy storage, to ensure full and friendly consumption of photovoltaic clean energy,η dc-dc It is the two-stage photovoltaic DC / DC conversion efficiency; P PV (t)=η dc-dc P MPPT (t) P hess (t) is the battery P bat (t) and supercapacitor P cap (t) the sum; P hess (t)=P bat (t)+P cap (t) Both batteries and supercapacitors include charging, idle and discharging states. With discharge power as positive, the expressions for power loss are: Where P bat，c (t), P bat，d (t), P cap，c (t), P cap，d (t), η bat，c , η bat，d , η cap，c , η cap，d They are the charge / discharge power, DC / DC conversion efficiency, and energy conversion efficiency of the energy storage body of the battery and supercapacitor respectively; To avoid overcharge and deep discharge, calculate, P bat (t) and P cap (t) output range, and substitute it into the determination of P total (t) output range; The above formulas 1 and 2 are the charging process, P bat，clim and P cap，clim are the charging power limits of the battery and supercapacitor during time period t; C bat , C cap , P bat，cmax , P cap，cmax They are the rated capacity and maximum charging power of the battery and supercapacitor respectively; SOC bat，max and SOC cap，max are the upper limits of the state of charge of the battery and supercapacitor respectively; Δt is the duration of each calculation period; The above equations 3 and 4 are the discharge process, P bat，dlim and P cap，dlim are the discharge power limits of the battery and supercapacitor during time period t; P bat，dmax and P cap，dmax are the maximum discharge power of the battery and supercapacitor respectively; SOC bat，min and SOC cap，min They are the lower limits of the state of charge of the battery and supercapacitor respectively.

3. The optimization method of the hybrid energy storage system of the distribution network according to claim 1, characterized in that: The step 2 includes an upper layer expansion planning model and a lower layer distribution network reliability model; The objective function of the upper extended planning model aims to minimize the present value of the total investment and operating costs of the system within the planning period. The objective function consists of three parts. The first term of the function represents the current value of the investment cost under the assumption of a permanent or infinite planning horizon. When the equipment life expires, it is necessary to reinvest in the same equipment. The second term of the objective function is the present value of the operating cost within the planning scope. The third term of the objective function is the present value of the operating cost incurred after the planning time stage. Among them, E S is the present value of the total system cost; represents the total investment cost in year t, which is composed of the line investment cost, wind turbine investment cost, PV investment cost, substation construction cost, substation expansion cost and ESS investment cost; is the total annual operating cost of the substation, is the total electricity purchase cost of the system in year t, including the electricity purchase cost from the upper grid through the substation, the electricity purchase cost from renewable energy and ESS, is the annual network loss cost; I f represents the interest rate; The annual investment cost of the power grid is calculated as follows: in, represents the unit construction cost of the line, l ij is the length of the distribution network line ij, z ij is the 0-1 investment decision variable of line ij, which belongs to the set of distribution network candidate lines Ω CL ; is the 0-1 investment decision variable for wind turbines and PV, and The construction cost of wind turbines and PV; is the 0-1 investment decision variable related to substation construction and expansion, is the construction cost of substation investment and capacity expansion, Ω SUB is a node set including existing substations and candidate substations; 0-1 decision variable for investment in ESS construction, is the construction cost of ESS, Ω ESS is the candidate node set of ESS; f DL ,f W ,f PV ,f SUB ,f RE and f ESS Asset depreciation rates for various types of assets; The operation formula of the substation is: Where b∈Ω B is a set of scenarios with typical load levels throughout the year. and Δb represent the total annual hours and unit duration of simulation operation of typical scenario b; ξ e,b represents the probability of uncertain scenario e under load level b; is the unit operating cost of the substation; represents the square of the apparent load power of the substation in the random scenario e with load level b in year t; Substation capacity constraints; The active and reactive output of the substation is subject to the maximum active and reactive output limits. The maximum active and reactive output power limits of the substation can be determined by the above formula, which limits the square of the apparent power of the substation to be less than the square of its maximum capacity. The maximum capacity of the substation depends on the initial installation capacity and the capacity of the upgraded expansion. Among them, P Gmax and Q Gmax Respectively represent the maximum active and reactive output power of the substation; S Gmax and S Gre Represents the installed capacity and expansion capacity of the substation; α G is the maximum power factor allowed by the substation set in advance, α G ∈[0,1].

4. The optimization method of the hybrid energy storage system of the distribution network according to claim 3 is characterized in that: The lower distribution network reliability model includes a lower objective function, and the lower scheduling strategy of the lower objective function can be expressed as minimizing the penalty cost of EENS under random fault operation conditions, as follows: Among them, C EENS,t represents the total annual cost of EENS, E EENS is the present value of the total cost of EENS; c voll represents the unit load loss penalty price; ρ k represents the probability that the system operates under an occasional random failure event k; P VOLL,k,i,t,b represents the load loss of node i under accidental event scenario k; Operation constraints restricted by upper-level decision-making; Since the investment strategy has been determined by the upper-level model, the distribution network can only follow the construction layout plan determined by the upper layer in the fault operation mode. The output characteristics of renewable generators and ESS under the accidental event k constrain whether the substation operates normally after the fault event k occurs. When the substation fails, the substation load power is always 0, and the maximum load loss value in all faults is screened and passed to the upper-level planning model; in, and They represent the charging and discharging power of ESS at fault event k, and they must satisfy the constraints related to ESS operation; C apESS,k,e,i,t,b is the real-time operating capacity of the ESS when fault k occurs. The symbol * shown in the formula represents the decision variables in the upper-level planning model, which are transmitted to the lower level and converted into fixed parameters.

5. The optimization method of the hybrid energy storage system for the distribution network according to claim 1, characterized in that: The step three includes setting the optimization operation cycle to 24 hours, with 1 minute as the optimization time period. Each optimization adopts the PHESS internal parameter value at the initial moment and the distribution network real-time power flow data provided by the distribution network automation system. Considering the uncertainty of photovoltaic output, the PHESS optimization distribution network operation strategy is based on the source-grid economic operation and grid-connected node voltage over-limit management models of distribution network operation. The optimization operation focuses on safety issues first, and the node voltage over-limit management priority in the strategy is higher than economic operation. The source-grid economic operation model takes the minimum source-grid loss as the objective function. The source-grid loss is determined by the distribution network loss P L,loss (t) and P PHESS,loss (t) Operation loss composition; P L，loss (t) represents the sum of active injection power of each node in the system; P PHESS,loss (t) includes PV DC / DC losses, energy storage operation losses and grid-connected AC / DC losses. The objective function is as follows: F=min[P Lloss (t)+P PHESS,loss (t)] P PHESS，loss (t)=(1-1th dc-dc )P MPPT (t)+(1-η bat，c )P bat，c (t)+(1-η bat,d )P bat，d (t)+(1-η cap，c )P cap，c (t)+(1-η cap，d )P cap，d (t)+ξ(t)ΔP s-loss +(1-th dc-ac )P MPPT [P PV (t)+P hess (t)] Where N is the number of system nodes; P i (t) is the injected active power of node i at time t; ξ(t) is the dimension of the battery charge and discharge state change, and ξ(t) is 0 or 1; ΔP s-loss is the loss generated when the energy storage charging and discharging state is switched at time t, which is considered as a constant; The constraints include system power flow constraints and substation export power constraints; The system power flow constraint is, Where i, j are the system node numbers; U i (t),U j (t) is the voltage amplitude of node i, j at time t; G ij ,B ij are the mutual conductance and mutual susceptance between nodes i and j respectively; δ ij (t) is the phase difference between nodes i and j at time t; P K,i (t),Q K,i (t),P D,i (t),Q D,i (t),P total，t (t),Q total，t (t),P PV,i ,P WT,i They are respectively the active and reactive power of the substation outlet at node i at time t, the active and reactive power of the load, the active and reactive power of the PHESS, and the grid-connected power of the distributed photovoltaic and wind power systems; The substation export power constraint is: Where P K,max ,P K,min ,Q K,max , Q K,min They are the upper and lower limits of active and reactive power at the substation export respectively.

6. The optimization method of the hybrid energy storage system for the distribution network according to claim 1, characterized in that: The step 4 includes classifying the operating state of the HESS according to the SOC state of the battery and the supercapacitor and their charge and discharge state, and adopting different types of HESS control strategies for different operating states, including multi-objective frequency division control, internal energy coordination control and stopping operation; when the SOC of the battery and the supercapacitor are both in normal state, the HESS adopts a multi-objective frequency division control strategy to give full play to the complementarity of the technical advantages of the battery and the supercapacitor and extend the operating life of the HESS; when the SOC of the battery and the supercapacitor is only set to meet the independent operating conditions, the HESS adopts an internal energy coordination control strategy to ensure that the battery and the supercapacitor operate within their respective safety ranges; if the SOC of the battery and the supercapacitor does not meet the operating conditions, the HESS stops optimizing operation, and charges and discharges the HESS to return to normal working state as soon as possible.

7. The optimization method of the hybrid energy storage system of the distribution network according to claim 6, characterized in that: The upper and lower limits of the SOC of the battery and supercapacitor are [0.85, 0.15] and [0.95, 0.05] respectively; the specific steps are as follows: First, adopt PHESS to participate in the distribution network optimization operation strategy and calculate the output power P of HESS. hess (t), and according to P hess (t) Determine the operating status of the HESS; Second, according to the HESS state classification, combined with the SOC of the battery and supercapacitor at time t, the corresponding control strategy of the HESS is determined; Third, according to the different control strategies of the HESS determined in step 2, the charging and discharging power of the battery and the supercapacitor are calculated respectively.

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