Rural power grid distributed energy storage double-layer planning method and device considering light-biogas collaboration

Abstract

The invention discloses a rural power grid distributed energy storage double-layer planning method and device considering light-biogas collaboration, and the method comprises the steps: constructing a light-biogas collaboration double-layer planning model containing distributed energy storage access, enabling an upper model to achieve the minimum annual comprehensive cost as a target, carrying out the locating and sizing of the distributed energy storage, and enabling the upper model to be the minimum annual comprehensive cost as a target; the lower layer model takes the feeder line peak valley difference, the voltage stability, the network loss and the voltage deviation as targets to carry out collaborative operation optimization on light, biogas and storage, and finally the bilevel planning model is solved to complete the optimal configuration of energy storage and the operation optimization of the rural power distribution network. According to the scheme, the rural comprehensive energy system is constructed, the safety problems of line overload, voltage bidirectional out-of-limit and the like caused by large-scale disordered access of distributed resources in rural areas and transformation development of rural industrial loads are solved, and the method has great significance in promoting two strategic tasks of new energy system construction and rural revitalization.

Description

Technical Field

[0001] The present invention belongs to the field of distribution network optimization, and in particular to a method and device for dual-layer planning of distributed energy storage in rural power grids considering solar-biogas synergy. Background Art

[0002] The large-scale disorderly access of distributed photovoltaics in rural areas and the transformation and development of rural industrial loads have caused rural distribution networks to gradually face safety issues such as line overload and bidirectional voltage over-limit caused by the mismatch between distributed power generation resource output and load demand, which has brought challenges to the stable operation of rural distribution networks.

[0003] Considering the abundant biomass energy resources in rural areas, the centralized processing and reuse of agricultural waste using advanced technologies can both reduce environmental pollution and improve the quality of rural electricity, making it a key component of rural green energy systems. However, biogas power generation based on biomass energy, like distributed photovoltaic power generation, is intermittent. Therefore, appropriate energy storage is required within the distribution network to balance the power of distributed photovoltaic and biogas power generation. Through the coordinated optimization of photovoltaic, biogas, and storage, the rural distribution network can achieve peak load shaving and valley filling, addressing safety issues such as line overloads and bidirectional voltage overshoots, and improving the load-bearing capacity of distributed photovoltaic and rural industries. Summary of the Invention

[0004] To address the current problems in rural power distribution networks, the present invention provides a two-tier planning method and device for distributed energy storage in rural power grids that considers solar-biogas synergy, aiming to improve the voltage quality of rural power distribution networks and enhance their carrying capacity for distributed power generation and new industrial loads.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A two-tier planning method for distributed energy storage in rural power grids considering solar-biogas synergy includes the following steps:

[0007] A two-layer planning model was established, consisting of an upper-layer model (planning layer) and a lower-layer model (operation layer). The upper-layer model selects the site and sizes the distributed energy storage system with the goal of minimizing annual comprehensive costs, while the lower-layer model optimizes the coordinated operation of solar-gas-storage based on feeder peak-to-valley differences, voltage stability, network losses, and voltage deviation.

[0008] The two-level programming model is solved. The upper-level model uses a particle swarm algorithm to randomly select distributed energy storage access locations and calculates the current objective function value based on the solar-biogas-storage operation optimization results output by the lower-level model, thereby optimizing the site selection and sizing of the energy storage. The lower-level model uses a second-order cone programming method to solve the solar-biogas-storage coordinated operation optimization problem based on the energy storage site selection and sizing results output by the upper-level model, and obtains the active power and reactive power values of the solar-biogas-storage.

[0009] Furthermore, the objective function of the upper model is:

[0010] C=min(C ES / n+C PA +C MA +C loss )

[0011] Among them, C is the annual comprehensive cost, C ES is the total construction investment cost of energy storage; n is the planned service life; C PA is the daily labor maintenance cost; C MA is the maintenance cost; C loss Cost of normal operation loss;

[0012] Total energy storage construction investment cost C ES for:

[0013]

[0014] Among them, C ap，i is the unit capacity investment cost of energy storage, C i is the capacity of energy storage i, j is the number of distributed energy storage;

[0015] Daily labor maintenance cost C PA for:

[0016] C PA =C PAH (1+γ1) t-1 +C PAM (1+γ2) t-1

[0017] Among them, C PAH and C PAM They represent the operating labor costs and other operating and maintenance costs in the first year of operation respectively; γ1 and γ2 represent the annual change rates of operating labor costs and other operating and maintenance costs respectively;

[0018] Maintenance cost C MA for:

[0019] C MA =r(t)[C MAO (t)P(t)+CMAMR (t)(1-P(t))]

[0020] Where r(t) is the failure rate of distributed energy storage; C MAO , C MAMR They represent the average cost of a major overhaul and a spot check of distributed energy storage, respectively; P(t) is the probability of a major overhaul after a distributed energy storage failure;

[0021] Normal operation loss cost C loss for:

[0022]

[0023] Among them, λ(t) is the price of electricity purchased by the power grid from the power plant; P ES (t) is the total loss of distributed energy storage.

[0024] Furthermore, the constraints of the upper model are:

[0025]

[0026] Among them, x i The distributed energy storage capacity configured for the i-th distribution network node; P i-max P is the maximum energy storage capacity allowed to be configured for the i-th distribution network node; max is the maximum energy storage capacity allowed to be connected to the system, n1 is the number of distribution network nodes; g(x i )=0 is the power balance equality constraint.

[0027] Furthermore, the objective function f of the lower layer is:

[0028] f=min(α1f1+α2f2+α3f3+α4f4)

[0029] Among them, f1 represents the minimum feeder peak-to-valley difference, f2 represents the optimal voltage stability index, f3 represents the minimum network loss, and f4 represents the minimum voltage deviation; α1, α2, α3, and α4 are the weight coefficients of each sub-objective function, and the sum of the four is 1;

[0030] The expression of minimum feeder peak-to-valley difference is:

[0031] f1=minΔP max

[0032] Where ΔP max Indicates the maximum peak-to-valley difference of the feeder;

[0033] The optimal voltage stability index expression is:

[0034] f2=minL u,max

[0035] Among them, L u,max Indicates the maximum voltage stability index L among all branches of the distribution network u , L u The calculation formula is:

[0036]

[0037] Among them, U i 、U j Represent the voltage amplitudes at nodes i and j respectively; δ represents the phase angle difference between the two nodes;

[0038] The minimum network loss expression is:

[0039]

[0040] Among them, P loss,t is the active network loss in the tth period, N T is the number of periods for calculation, Δt is the time step of each period;

[0041] The minimum voltage deviation expression is:

[0042] f4=min(|1-U up |+|1-U down |)

[0043] Among them, U up 、U down are the maximum and minimum node voltage amplitudes in the system, respectively.

[0044] Furthermore, the constraints of the lower model include distribution transformer capacity constraints, system power flow constraints, distributed photovoltaic operation constraints, biogas power generation operation constraints, distributed energy storage operation constraints, and distribution system safety operation constraints, where:

[0045] Distribution transformer capacity constraints:

[0046]

[0047] Among them, P t,i is the active power of the load at the i-th node at the t-th moment; η T is the efficiency of the distribution transformer; S T is the capacity of the distribution transformer; cosθ is the power factor;

[0048] The system power flow constraint is:

[0049]

[0050] Among them, P G,t,i and Q G,t,iare the active power and reactive power of the distributed generation at node i at time t; P t,i and Q t,i is the active power and reactive power of the load of the i-th node at time t; Π(i) is the combination of all nodes connected to node i; U t,i and U t,j is the voltage value of node i and node j at time t; G i,j and B i,j are the conductance and susceptance between nodes i and j respectively; θ t,i,j is the voltage phase angle difference between node i and node j at time t;

[0051] The operating constraints of distributed photovoltaics are:

[0052]

[0053] in, and They represent the actual active power and the maximum allowed active power generated by the photovoltaic on node i at time t respectively; represents the reactive power generated by the photovoltaic power plant at node i at time t; tanθ PV Indicates photovoltaic power factor;

[0054] The operating constraints of biogas power generation are:

[0055]

[0056] in, and They represent the actual active power and the maximum allowable active power generated by the biogas power generation on node i at time t; V i,t and V i,max represent the actual gas capacity and maximum gas capacity of biogas at node i respectively;

[0057] The operating constraints of distributed energy storage are:

[0058]

[0059] in, and represents the active power and reactive power of distributed energy storage i at time t; represents the maximum capacity of distributed energy storage i;

[0060] The safe operation constraints of the power distribution system are:

[0061]

[0062] in, and Respectively represent the minimum and maximum allowable voltages of node i; U t,i represents the actual voltage of node i at time t; represents the maximum allowable current between nodes i and j; I t,ij represents the actual current between nodes i and j at time t.

[0063] A two-layer planning device for distributed energy storage in rural power grids considering solar-biogas synergy, comprising:

[0064] A two-layer planning model establishment module is used to establish an upper-layer model (planning layer) and a lower-layer model (operation layer). The upper-layer model selects the site and determines the capacity of distributed energy storage with the goal of minimizing annual comprehensive costs. The lower-layer model optimizes the coordinated operation of solar-gas-storage based on feeder peak-to-valley differences, voltage stability, network losses, and voltage deviation.

[0065] The two-level programming model solving module uses a particle swarm algorithm to randomly select distributed energy storage access locations for the upper-level model. It then calculates the current objective function value based on the solar-biogas-storage operation optimization results output by the lower-level model, thereby optimizing the siting and sizing of energy storage. For the lower-level model, a second-order cone programming method is used to solve the solar-biogas-storage coordinated operation optimization problem based on the energy storage siting and sizing results output by the upper-level model, and to obtain the active and reactive power values of the solar-biogas-storage system.

[0066] Furthermore, the objective function of the upper model is:

[0067] C=min(C ES / n+C PA +C MA +C loss )

[0068] Among them, C is the annual comprehensive cost, C ES is the total construction investment cost of energy storage; n is the planned service life; C PA is the daily labor maintenance cost; C MA is the maintenance cost; C loss Cost of normal operation loss;

[0069] Total energy storage construction investment cost C ES for:

[0070]

[0071] Among them, C ap，i is the unit capacity investment cost of energy storage, C i is the capacity of energy storage i, j is the number of distributed energy storage;

[0072] Daily labor maintenance cost C PA for:

[0073] C PA =C PAH (1+γ1) t-1 +C PAM (1+γ2) t-1

[0074] Among them, C PAH and C PAM They represent the operating labor costs and other operating and maintenance costs in the first year of operation respectively; γ1 and γ2 represent the annual change rates of operating labor costs and other operating and maintenance costs respectively;

[0075] Maintenance cost C MA for:

[0076] C MA =r(t)[C MAO (t)P(t)+C MAMR (t)(1-P(t))]

[0077] Where r(t) is the failure rate of distributed energy storage; C MAO , C MAMR They represent the average cost of a major overhaul and a spot check of distributed energy storage, respectively; P(t) is the probability of a major overhaul after a distributed energy storage failure;

[0078] Normal operation loss cost C loss for:

[0079]

[0080] Among them, λ(t) is the price of electricity purchased by the power grid from the power plant; P ES (t) is the total loss of distributed energy storage.

[0081] Furthermore, the constraints of the upper model are:

[0082]

[0083] Among them, x i The distributed energy storage capacity configured for the i-th distribution network node; P i-max P is the maximum energy storage capacity allowed to be configured for the i-th distribution network node; max is the maximum energy storage capacity allowed to be connected to the system, n1 is the number of distribution network nodes; g(x i )=0 is the power balance equality constraint.

[0084] Furthermore, the objective function f of the lower layer is:

[0085] f=min(α1f1+α2f2+α3f3+α4f4)

[0086] Among them, f1 represents the minimum feeder peak-to-valley difference, f2 represents the optimal voltage stability index, f3 represents the minimum network loss, and f4 represents the minimum voltage deviation; α1, α2, α3, and α4 are the weight coefficients of each sub-objective function, and the sum of the four is 1;

[0087] The expression of minimum feeder peak-to-valley difference is:

[0088] f1=minΔP max

[0089] Where ΔP max Indicates the maximum peak-to-valley difference of the feeder;

[0090] The optimal voltage stability index expression is:

[0091] f2=minL u,max

[0092] Among them, L u,max Indicates the maximum voltage stability index L among all branches of the distribution network u , L u The calculation formula is:

[0093]

[0094] Among them, U i 、U j Represent the voltage amplitudes at nodes i and j respectively; δ represents the phase angle difference between the two nodes;

[0095] The minimum network loss expression is:

[0096]

[0097] Among them, P loss,t is the active network loss in the tth period, N T is the number of periods for calculation, Δt is the time step of each period;

[0098] The minimum voltage deviation expression is:

[0099] f4=min(|1-U up |+|1-U down |)

[0100] Among them, U up 、U down are the maximum and minimum node voltage amplitudes in the system, respectively.

[0101] Furthermore, the constraints of the lower model include distribution transformer capacity constraints, system power flow constraints, distributed photovoltaic operation constraints, biogas power generation operation constraints, distributed energy storage operation constraints, and distribution system safety operation constraints, where:

[0102] Distribution transformer capacity constraints:

[0103]

[0104] Among them, P t,i is the active power of the load at the i-th node at the t-th moment; η T is the efficiency of the distribution transformer; S T is the capacity of the distribution transformer; cosθ is the power factor;

[0105] The system power flow constraint is:

[0106]

[0107] Among them, P G,t,i and Q G,t,i are the active power and reactive power of the distributed generation at node i at time t; P t,i and Q t,i is the active power and reactive power of the load of the i-th node at time t; Π(i) is the combination of all nodes connected to node i; U t,i and U t,j is the voltage value of node i and node j at time t; G i,j and B i,j are the conductance and susceptance between nodes i and j respectively; θ t,i,j is the voltage phase angle difference between node i and node j at time t;

[0108] The operating constraints of distributed photovoltaics are:

[0109]

[0110] in, and They represent the actual active power and the maximum allowed active power generated by the photovoltaic on node i at time t respectively; represents the reactive power generated by the photovoltaic power plant at node i at time t; tanθ PV Indicates photovoltaic power factor;

[0111] The operating constraints of biogas power generation are:

[0112]

[0113] in, and They represent the actual active power and the maximum allowable active power generated by the biogas power generation on node i at time t; V i,t and V i,max represent the actual gas capacity and maximum gas capacity of biogas at node i respectively;

[0114] The operating constraints of distributed energy storage are:

[0115]

[0116] in, and represents the active power and reactive power of distributed energy storage i at time t; represents the maximum capacity of distributed energy storage i;

[0117] The safe operation constraints of the power distribution system are:

[0118]

[0119] in, and Respectively represent the minimum and maximum allowable voltages of node i; U t,i represents the actual voltage of node i at time t; represents the maximum allowable current between nodes i and j; I t,ij represents the actual current between nodes i and j at time t.

[0120] Compared with the prior art, the present invention has the following beneficial effects:

[0121] The present invention takes into account the distributed photovoltaic and biogas power generation characteristics of rural distribution networks, and rationally introduces energy storage to reduce the peak-to-valley difference of rural distribution networks and smooth the power fluctuations of intermittent energy, thus solving the problem of distributed photovoltaic absorption difficulties; through the coordinated optimization of photovoltaic-biogas-storage resources, the voltage quality of rural distribution networks is improved, and a flexible power regulation method is provided for rural distribution networks; at the same time, based on the complementary characteristics of different energy sources, the economic losses caused by energy surplus are reduced, the carrying capacity of rural distribution networks for distributed photovoltaic access and new industrial loads is enhanced, the operating economy of rural distribution networks is improved, and the energy consumption cost is reduced.

[0122] The application of the scheme of the present invention can reduce the pollution of agricultural waste to the environment in rural areas, while reducing dependence on traditional fossil energy, helping to promote the dual carbon goals and the national strategy of rural revitalization, and providing a new solution for promoting the transformation of my country's rural energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 This is a schematic diagram of the topological structure of a 21-node system in a certain area of the embodiment;

[0124] Figure 2 Schematic diagram of a two-tier planning framework for distributed energy storage in rural power grids considering solar-biogas synergy.

[0125] Figure 3This is a schematic diagram of the optimized access location for energy storage in the embodiment;

[0126] Figure 4 Schematic diagram of voltage distribution before and after system optimization in a certain scenario of the embodiment. DETAILED DESCRIPTION

[0127] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0128] Reference Figure 1 , Figure 1 This is a schematic diagram of the topological structure of a 21-node system in a certain area. The network includes conventional loads and distributed power generation equipment such as photovoltaic and biogas power generation. The access locations and parameters of the distributed power generation equipment are shown in Table 1.

[0129] Table 1 Distributed power generation equipment access nodes and parameters

[0130]

[0131] Reference Figure 2 , Figure 2 A two-tier planning method for distributed energy storage in rural power grids that considers solar-biogas synergy is proposed. This method is characterized by establishing a two-tier planning model, including an upper-tier model as the planning layer and a lower-tier model as the operation layer. The upper-tier model selects the site and sizes the distributed energy storage with the goal of minimizing annual comprehensive costs, while the lower-tier model optimizes the coordinated operation of solar-biogas-storage with feeder peak-to-valley differences, voltage stability, network losses, and voltage deviation as objectives.

[0132] The two-level programming model is solved. The upper-level model uses a particle swarm algorithm to randomly select distributed energy storage access locations and calculates the current objective function value based on the solar-biogas-storage operation optimization results output by the lower-level model, thereby optimizing the site selection and sizing of the energy storage. The lower-level model uses a second-order cone programming method to solve the solar-biogas-storage coordinated operation optimization problem based on the energy storage site selection and sizing results output by the upper-level model, and obtains the active power and reactive power values of the solar-biogas-storage.

[0133] Furthermore, the objective function of the upper model is:

[0134] C=min(C ES / n+C PA +C MA +C loss )

[0135] Among them, C is the annual comprehensive cost, C ES is the total construction investment cost of energy storage; n is the planned service life; C PA is the daily labor maintenance cost; C MA is the maintenance cost; C loss Cost of normal operation loss;

[0136] Total energy storage construction investment cost C ES for:

[0137]

[0138] Among them, C ap，i is the unit capacity investment cost of energy storage, C i is the capacity of energy storage i, j is the number of distributed energy storage;

[0139] Daily labor maintenance cost C PA for:

[0140] C PA =C PAH (1+γ1) t-1 +C PAM (1+γ2) t-1

[0141] Among them, C PAH and C PAM They represent the operating labor costs and other operating and maintenance costs in the first year of operation respectively; γ1 and γ2 represent the annual change rates of operating labor costs and other operating and maintenance costs respectively;

[0142] Maintenance cost C MA for:

[0143] C MA =r(t)[C MAO (t)P(t)+C MAMR (t)(1-P(t))]

[0144] Where r(t) is the failure rate of distributed energy storage; C MAO , C MAMR They represent the average cost of a major overhaul and a spot check of distributed energy storage, respectively; P(t) is the probability of a major overhaul after a distributed energy storage failure;

[0145] Normal operation loss cost C loss for:

[0146]

[0147] Among them, λ(t) is the price of electricity purchased by the power grid from the power plant; P ES (t) is the total loss of distributed energy storage.

[0148] Furthermore, the constraints of the upper model are:

[0149]

[0150] Among them, x i The distributed energy storage capacity configured for the i-th distribution network node; P i-max P is the maximum energy storage capacity allowed to be configured for the i-th distribution network node; max is the maximum energy storage capacity allowed to be connected to the system, n1 is the number of distribution network nodes; g(x i )=0 is the power balance equality constraint.

[0151] Furthermore, the objective function f of the lower layer is:

[0152] f=min(α1f1+α2f2+α3f3+α4f4)

[0153] Among them, f1 represents the minimum feeder peak-to-valley difference, f2 represents the optimal voltage stability index, f3 represents the minimum network loss, and f4 represents the minimum voltage deviation; α1, α2, α3, and α4 are the weight coefficients of each sub-objective function, and the sum of the four is 1;

[0154] The expression of minimum feeder peak-to-valley difference is:

[0155] f1=minΔP max

[0156] Where ΔP max Indicates the maximum peak-to-valley difference of the feeder;

[0157] The optimal voltage stability index expression is:

[0158] f2=minL u,max

[0159] Among them, L u,max Indicates the maximum voltage stability index L among all branches of the distribution network u , L u The calculation formula is:

[0160]

[0161] Among them, U i 、U j Represent the voltage amplitudes at nodes i and j respectively; δ represents the phase angle difference between the two nodes;

[0162] The minimum network loss expression is:

[0163]

[0164] Among them, Ploss,t is the active network loss in the tth period, N T is the number of periods for calculation, Δt is the time step of each period;

[0165] The minimum voltage deviation expression is:

[0166] f4=min(|1-U up |+|1-U down |)

[0167] Among them, U up 、U down are the maximum and minimum node voltage amplitudes in the system, respectively.

[0168] Furthermore, the constraints of the lower model include distribution transformer capacity constraints, system power flow constraints, distributed photovoltaic operation constraints, biogas power generation operation constraints, distributed energy storage operation constraints, and distribution system safety operation constraints, where:

[0169] Distribution transformer capacity constraints:

[0170]

[0171] Among them, P t,i is the active power of the load at the i-th node at the t-th moment; η T is the efficiency of the distribution transformer; S T is the capacity of the distribution transformer; cosθ is the power factor;

[0172] The system power flow constraint is:

[0173]

[0174] Among them, P G,t,i and Q G,t,i are the active power and reactive power of the distributed generation at node i at time t; P t,i and Q t,i is the active power and reactive power of the load of the i-th node at time t; Π(i) is the combination of all nodes connected to node i; U t,i and U t,j is the voltage value of node i and node j at time t; G i,j and B i,j are the conductance and susceptance between nodes i and j respectively; θ t,i,j is the voltage phase angle difference between node i and node j at time t;

[0175] The operating constraints of distributed photovoltaics are:

[0176]

[0177] in, and They represent the actual active power and the maximum allowed active power generated by the photovoltaic on node i at time t respectively; represents the reactive power generated by the photovoltaic power plant at node i at time t; tanθ PV Indicates photovoltaic power factor;

[0178] The operating constraints of biogas power generation are:

[0179]

[0180] in, and They represent the actual active power and the maximum allowable active power generated by the biogas power generation on node i at time t; V i,t and V i,max represent the actual gas capacity and maximum gas capacity of biogas at node i respectively;

[0181] The operating constraints of distributed energy storage are:

[0182]

[0183] in, and represents the active power and reactive power of distributed energy storage i at time t; represents the maximum capacity of distributed energy storage i;

[0184] The safe operation constraints of the power distribution system are:

[0185]

[0186] in, and Respectively represent the minimum and maximum allowable voltages of node i; U t,i represents the actual voltage of node i at time t; represents the maximum allowable current between nodes i and j; I t,ij represents the actual current between nodes i and j at time t.

[0187] Reference Figure 3 , Figure 3 This is a schematic diagram of the optimized energy storage access location after adopting the double-layer optimization algorithm of the embodiment. The optimized energy storage access location and capacity parameters are shown in Table 2.

[0188] Table 2 Energy storage access location and capacity

[0189]

[0190] Reference Figure 4 , Figure 4 Figure 2 shows the voltage distribution before and after system optimization for a specific scenario in this example. The optimized system voltage distribution is significantly better than before. Compared to existing technologies, this invention rationally incorporates energy storage to regulate the voltage quality of the distribution network. By synergistically optimizing solar-gas-storage resources, it smooths the power fluctuations of intermittent energy sources, provides a flexible power regulation method for rural distribution networks, and enhances their capacity to handle distributed photovoltaic access and new industrial loads. This improves the operational economics of rural distribution networks and reduces energy consumption costs.

[0191] The embodiment of the present invention further provides a two-tier planning device for distributed energy storage in rural power grids considering solar-biogas synergy, comprising:

[0192] A two-layer planning model establishment module is used to establish an upper-layer model (planning layer) and a lower-layer model (operation layer). The upper-layer model selects the site and determines the capacity of distributed energy storage with the goal of minimizing annual comprehensive costs. The lower-layer model optimizes the coordinated operation of solar-gas-storage based on feeder peak-to-valley differences, voltage stability, network losses, and voltage deviation.

[0193] The two-level programming model solving module uses a particle swarm algorithm to randomly select distributed energy storage access locations for the upper-level model. It then calculates the current objective function value based on the solar-biogas-storage operation optimization results output by the lower-level model, thereby optimizing the siting and sizing of energy storage. For the lower-level model, a second-order cone programming method is used to solve the solar-biogas-storage coordinated operation optimization problem based on the energy storage siting and sizing results output by the upper-level model, and to obtain the active and reactive power values of the solar-biogas-storage system.

[0194] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principles and purpose of the present invention. The scope of protection of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. All implementation schemes within its scope shall be subject to the constraints of the present invention.

Claims

1. A two-tier planning method for distributed energy storage in rural power grids considering solar-biogas synergy, characterized by: The steps include: A two-layer planning model was established, consisting of an upper-layer model (planning layer) and a lower-layer model (operation layer). The upper-layer model selects the site and sizes the distributed energy storage system with the goal of minimizing annual comprehensive costs, while the lower-layer model optimizes the coordinated operation of solar-gas-storage based on feeder peak-to-valley differences, voltage stability, network losses, and voltage deviation. The two-level programming model is solved. The upper-level model uses a particle swarm algorithm to randomly select distributed energy storage access locations and calculates the current objective function value based on the solar-biogas-storage operation optimization results output by the lower-level model, thereby optimizing the site selection and sizing of the energy storage. The lower-level model uses a second-order cone programming method to solve the solar-biogas-storage coordinated operation optimization problem based on the energy storage site selection and sizing results output by the upper-level model, and obtains the active power and reactive power values of the solar-biogas-storage.

2. The method for planning distributed energy storage in rural power grids considering solar-biogas synergy according to claim 1 is characterized in that: The objective function of the upper model is: C=min(C ES / n+C PA +C MA +C loss ) Among them, C is the annual comprehensive cost, C ES is the total construction investment cost of energy storage; n is the planned service life; C PA is the daily labor maintenance cost; C MA is the maintenance cost; C loss Cost of normal operation loss; Total energy storage construction investment cost C ES for: Among them, C ap，i is the unit capacity investment cost of energy storage, C i is the capacity of energy storage i, j is the number of distributed energy storage; Daily labor maintenance cost C PA for: C PA =C PAH (1+γ1) t-1 +C PAM (1+γ2) t-1 Among them, C PAH and C PAM They represent the operating labor costs and other operating and maintenance costs in the first year of operation respectively; γ1 and γ2 represent the annual change rates of operating labor costs and other operating and maintenance costs respectively; Maintenance cost C MA for: C MA =r(t)[C MAO (t)P(t)+C MAMR (t)(1-P(t))] Where r(t) is the failure rate of distributed energy storage; C MAO , C MAMR They represent the average cost of a major overhaul and a spot check of distributed energy storage, respectively; P(t) is the probability of a major overhaul after a distributed energy storage failure; Normal operation loss cost C loss for: Among them, λ(t) is the price of electricity purchased by the power grid from the power plant; P ES (t) is the total loss of distributed energy storage.

3. The method for planning distributed energy storage in rural power grids considering solar-biogas synergy according to claim 2 is characterized in that: The constraints of the upper model are: Among them, x i The distributed energy storage capacity configured for the i-th distribution network node; P i-max P is the maximum energy storage capacity allowed to be configured for the i-th distribution network node; max is the maximum energy storage capacity allowed to be connected to the system, n1 is the number of distribution network nodes; g(x i )=0 is the power balance equality constraint.

4. The method for planning distributed energy storage in rural power grids considering solar-biogas synergy according to claim 1 is characterized in that: The objective function f of the lower layer is: f=min(α1f1+α2f2+α3f3+α4f4) Among them, f1 represents the minimum feeder peak-to-valley difference, f2 represents the optimal voltage stability index, f3 represents the minimum network loss, and f4 represents the minimum voltage deviation; α1, α2, α3, and α4 are the weight coefficients of each sub-objective function, and the sum of the four is 1; The expression of minimum feeder peak-to-valley difference is: f1=minΔP max Where ΔP max Indicates the maximum peak-to-valley difference of the feeder; The optimal voltage stability index expression is: f2=minL u,max Among them, L u,max Indicates the maximum voltage stability index L among all branches of the distribution network u , L u The calculation formula is: Among them, U i 、U j Represent the voltage amplitudes at nodes i and j respectively; δ represents the phase angle difference between the two nodes; The minimum network loss expression is: Among them, P loss,t is the active network loss in the tth period, N T is the number of periods for calculation, Δt is the time step of each period; The minimum voltage deviation expression is: f4=min(|1-U up |+|1-U down |) Among them, U up 、U down are the maximum and minimum node voltage amplitudes in the system, respectively.

5. The method for planning distributed energy storage in rural power grids considering solar-biogas synergy according to claim 4 is characterized in that: The constraints of the lower model include distribution transformer capacity constraints, system power flow constraints, distributed photovoltaic operation constraints, biogas power generation operation constraints, distributed energy storage operation constraints, and distribution system safety operation constraints, among which: Distribution transformer capacity constraints: Among them, P t,i is the active power of the load at the i-th node at the t-th moment; η T is the efficiency of the distribution transformer; S T is the capacity of the distribution transformer; cosθ is the power factor; The system power flow constraint is: Among them, P G,t,i and Q G,t,i are the active power and reactive power of the distributed generation at node i at time t; P t,i and Q t,i is the active power and reactive power of the load of the i-th node at time t; Π(i) is the combination of all nodes connected to node i; U t,i and U t,j is the voltage value of node i and node j at time t; G i,j and B i,j are the conductance and susceptance between nodes i and j respectively; θ t,i,j is the voltage phase angle difference between node i and node j at time t; The operating constraints of distributed photovoltaics are: in, and They represent the actual active power and the maximum allowed active power generated by the photovoltaic on node i at time t respectively; represents the reactive power generated by the photovoltaic power plant at node i at time t; tanθ PV Indicates photovoltaic power factor; The operating constraints of biogas power generation are: in, and They represent the actual active power and the maximum allowable active power generated by the biogas power generation on node i at time t; V i,t and V i,max represent the actual gas capacity and maximum gas capacity of biogas at node i respectively; The operating constraints of distributed energy storage are: in, and represents the active power and reactive power of distributed energy storage i at time t; represents the maximum capacity of distributed energy storage i; The safe operation constraints of the power distribution system are: in, and Respectively represent the minimum and maximum allowable voltages of node i; U t,i represents the actual voltage of node i at time t; represents the maximum allowable current between nodes i and j; I t,ij represents the actual current between nodes i and j at time t.

6. A dual-layer planning device for distributed energy storage in rural power grids considering solar-biogas synergy, characterized by: include: A two-layer planning model establishment module is used to establish an upper-layer model (planning layer) and a lower-layer model (operation layer). The upper-layer model selects the site and determines the capacity of distributed energy storage with the goal of minimizing annual comprehensive costs. The lower-layer model optimizes the coordinated operation of solar-gas-storage based on feeder peak-to-valley differences, voltage stability, network losses, and voltage deviation. The two-level programming model solving module uses a particle swarm algorithm to randomly select distributed energy storage access locations for the upper-level model. It then calculates the current objective function value based on the solar-biogas-storage operation optimization results output by the lower-level model, thereby optimizing the siting and sizing of energy storage. For the lower-level model, a second-order cone programming method is used to solve the solar-biogas-storage coordinated operation optimization problem based on the energy storage siting and sizing results output by the upper-level model, and to obtain the active and reactive power values of the solar-biogas-storage system.

7. The dual-layer planning device for distributed energy storage in rural power grids considering solar-biogas synergy as claimed in claim 6, characterized in that: The objective function of the upper model is: C=min(C ES / n+C PA +C MA +C loss ) Among them, C is the annual comprehensive cost, C ES is the total construction investment cost of energy storage; n is the planned service life; C PA is the daily labor maintenance cost; C MA is the maintenance cost; C loss Cost of normal operation loss; Total energy storage construction investment cost C ES for: Among them, C ap，i is the unit capacity investment cost of energy storage, C i is the capacity of energy storage i, j is the number of distributed energy storage; Daily labor maintenance cost C PA for: C PA =C PAH (1+γ1) t-1 +C PAM (1+γ2) t-1 Among them, C PAH and C PAM They represent the operating labor costs and other operating and maintenance costs in the first year of operation respectively; γ1 and γ2 represent the annual change rates of operating labor costs and other operating and maintenance costs respectively; Maintenance cost C MA for: C MA =r(t)[C MAO (t)P(t)+C MAMR (t)(1-P(t))] Where r(t) is the failure rate of distributed energy storage; C MAO , C MAMR They represent the average cost of a major overhaul and a spot check of distributed energy storage, respectively; P(t) is the probability of a major overhaul after a distributed energy storage failure; Normal operation loss cost C loss for: Among them, λ(t) is the price of electricity purchased by the power grid from the power plant; P ES (t) is the total loss of distributed energy storage.

8. The dual-layer planning device for distributed energy storage in rural power grids considering solar-biogas synergy as claimed in claim 7, characterized in that: The constraints of the upper model are: Among them, x i The distributed energy storage capacity configured for the i-th distribution network node; P i-max P is the maximum energy storage capacity allowed to be configured for the i-th distribution network node; max is the maximum energy storage capacity allowed to be connected to the system, n1 is the number of distribution network nodes; g(x i )=0 is the power balance equality constraint.

9. The dual-layer planning device for distributed energy storage in rural power grids considering solar-biogas synergy as claimed in claim 6, characterized in that: The objective function f of the lower layer is: f=min(α1f1+α2f2+α3f3+α4f4) Among them, f1 represents the minimum feeder peak-to-valley difference, f2 represents the optimal voltage stability index, f3 represents the minimum network loss, and f4 represents the minimum voltage deviation; α1, α2, α3, and α4 are the weight coefficients of each sub-objective function, and the sum of the four is 1; The expression of minimum feeder peak-to-valley difference is: f1=minΔP max Where ΔP max Indicates the maximum peak-to-valley difference of the feeder; The optimal voltage stability index expression is: f2=minL u,max Among them, L u,max Indicates the maximum voltage stability index L among all branches of the distribution network u , L u The calculation formula is: Among them, U i 、U j Represent the voltage amplitudes at nodes i and j respectively; δ represents the phase angle difference between the two nodes; The minimum network loss expression is: Among them, P loss,t is the active network loss in the tth period, N T is the number of periods for calculation, Δt is the time step of each period; The minimum voltage deviation expression is: f4=min(|1-U up |+|1-U down |) Among them, U up 、U down are the maximum and minimum node voltage amplitudes in the system, respectively.

10. The dual-layer planning device for distributed energy storage in rural power grids considering solar-biogas synergy as claimed in claim 9, characterized in that: The constraints of the lower model include distribution transformer capacity constraints, system power flow constraints, distributed photovoltaic operation constraints, biogas power generation operation constraints, distributed energy storage operation constraints, and distribution system safety operation constraints, among which: Distribution transformer capacity constraints: Among them, P t,i is the active power of the load at the i-th node at the t-th moment; η T is the efficiency of the distribution transformer; S T is the capacity of the distribution transformer; cosθ is the power factor; The system power flow constraint is: Among them, P G,t,i and Q G,t,i are the active power and reactive power of the distributed generation at node i at time t; P t,i and Q t,i is the active power and reactive power of the load of the i-th node at time t; Π(i) is the combination of all nodes connected to node i; U t,i and U t,j is the voltage value of node i and node j at time t; G i,j and B i,j are the conductance and susceptance between nodes i and j respectively; θ t,i,j is the voltage phase angle difference between node i and node j at time t; The operating constraints of distributed photovoltaics are: in, and They represent the actual active power and the maximum allowed active power generated by the photovoltaic on node i at time t respectively; represents the reactive power generated by the photovoltaic power plant at node i at time t; tanθ PV Indicates photovoltaic power factor; The operating constraints of biogas power generation are: in, and They represent the actual active power and the maximum allowable active power generated by the biogas power generation on node i at time t; V i,t and V i,max represent the actual gas capacity and maximum gas capacity of biogas at node i respectively; The operating constraints of distributed energy storage are: in, and represents the active power and reactive power of distributed energy storage i at time t; represents the maximum capacity of distributed energy storage i; The safe operation constraints of the power distribution system are: in, and Respectively represent the minimum and maximum allowable voltages of node i; U t,i represents the actual voltage of node i at time t; represents the maximum allowable current between nodes i and j; I t,ij represents the actual current between nodes i and j at time t.

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