Double-layer optimization planning method and device for energy storage of electric power system

Abstract

The invention belongs to the technical field of power systems, and particularly discloses a double-layer optimization planning method and device for energy storage of a power system. In order to overcome the defect that collaborative planning of pumped storage and electrochemical energy storage in a novel power system is not researched at the same time in existing patents and literatures, the invention provides a method for constructing a pumped storage and electrochemical energy storage double-layer optimization planning model of the power system. According to the method, the constructed double-layer optimization planning model is constrained according to a Karush-Kuhn-Tucher condition, and a double-layer nonlinear optimization model is converted into a single-layer linear optimization model, so that collaborative planning of pumped storage and electrochemical energy storage can be realized, and efficient solution of the double-layer optimization planning model is also realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a double-layer optimization planning method and device for energy storage in power systems. Background Art

[0002] Electrochemical energy storage, with its fast power regulation and its ability to supply and store energy, has played a major role in smoothing intermittent energy power fluctuations, shaving peaks and filling valleys, improving voltage quality, and providing backup power. It is the key to the flexible regulation of widely connected renewable energy and network optimization in new power systems. Pumped storage is a relatively mature energy storage technology with large capacity, low cost per kilowatt-hour, and slow equipment depreciation. It is also the key to ensuring the safety of new power systems and promoting the consumption of new energy. However, existing patents and literature do not simultaneously study the coordinated planning of pumped storage and electrochemical energy storage in new power systems. Summary of the invention

[0003] In order to solve at least one of the problems mentioned in the above background technology, the present invention proposes a two-layer optimization planning method and device for energy storage in a power system.

[0004] A two-level optimization planning method for power system energy storage includes the following steps:

[0005] Step S1, establishing an operation characteristic model of pumped storage and electrochemical energy storage, wherein the formula of the constraint condition of the pumped storage operation characteristic model is:

[0006]

[0007]

[0008] In the formula, and are the power generation and pumping state variables of the pumped storage at node i in period t under scenario s, and are respectively the generating power, pumping power and total power of the pumped storage at node i in period t under scenario s, and are the pumped storage power generation power and pumping power upper limit at node i, respectively. and are the upper and lower storage capacities of pumped storage at node i in period t under scenario s, and are the minimum and maximum storage capacities of the pumped storage reservoir at node i, and are the minimum and maximum storage capacities of the pumped storage reservoir at node i, is the average water-to-electricity conversion coefficient for power generation, is the average water-electricity conversion coefficient of pumping, is the efficiency of pumped storage at node i, and Δt is the time scale.

[0009] The formula for the constraints of the electrochemical energy storage operation characteristic model is:

[0010]

[0011] In the formula, and are the charging and discharging state variables of the electrochemical energy storage at node i during period t under scenario s, and are the charging power, discharging power and total power of the electrochemical energy storage at node i in time period t under scenario s, and are the upper limits of the charging power and discharging power of the electrochemical energy storage at node i, is the amount of electrochemical energy stored at node i during period t under scenario s, and are the maximum and minimum capacity of electrochemical energy storage at node i, respectively, ES,c and η ES,f They are the charging efficiency and discharging efficiency of electrochemical energy storage, respectively.

[0012] This invention proposes a method for pumped storage and electrochemical energy storage operation characteristic model. The operation characteristic model constructed according to this method can fully reflect the operation mechanism and response characteristics of pumped storage and electrochemical energy storage.

[0013] Step S2, constructing a two-level nonlinear programming model for pumped storage and electrochemical energy storage in the power system. Specifically, the two-level nonlinear programming model includes an upper-level programming model and a lower-level operation model, wherein the objective function of the upper-level programming model is:

[0014] min C all =C inv +C ope (12)

[0015] C inv =C PS +C ES (13)

[0016]

[0017] In the formula, C inv is the investment cost, C ope is the operating cost, minC all To minimize the total annual cost, is the power of pumped storage planned at node i, and are the upper reservoir capacity and lower reservoir capacity of the pumped storage planned at node i, and are the unit power and unit storage capacity investment costs of pumped storage, R PS is the investment recovery factor of pumped storage, and are the power and capacity of the electrochemical energy storage planned at node i, and are the unit power and unit capacity investment costs of electrochemical energy storage, R ES is the electrochemical energy storage investment recovery coefficient, r is the discount rate, τ PS and τ ES They are the lifespan of pumped storage and electrochemical energy storage, respectively.

[0018] The constraints of the upper-level planning model include pumped storage planning constraints and electrochemical energy storage planning constraints, among which,

[0019] The formula for pumped storage planning constraints is:

[0020]

[0021] In the formula, The power upper limit of the pumped storage planned for node i, and are the upper and lower reservoir capacity limits of the pumped storage planned for node i respectively.

[0022] The formula for electrochemical energy storage planning constraints is:

[0023]

[0024] In the formula, The power upper limit of the electrochemical energy storage planned for node i, The upper capacity limit of the electrochemical energy storage planned for node i.

[0025] The objective function of the lower-level running model is:

[0026]

[0027]

[0028] In the formula, minC ope To minimize operating costs, is the fuel cost of thermal power units, is the start-up and shutdown cost of thermal power units, C res Penalty costs for wind and solar curtailment, is the power of thermal power unit k in period t under scenario s, is the operating state variable of thermal power unit k in period t under scenario s, and is the coal cost coefficient of thermal power unit k, is the number of days of scenario s, is the start-up and shutdown cost of thermal power unit k, and are the predicted output and actual output of photovoltaic power station k in time period t under scenario s, and are the predicted output and actual output of wind farm k in period t under scenario s, c PV and c WT are the penalty cost coefficients for abandoning solar power and abandoning wind power respectively.

[0029] The constraints of the lower-level operation model include power flow constraints, thermal power operation constraints, photovoltaic operation constraints, wind power operation constraints, pumped storage operation constraints, electrochemical energy storage operation constraints and spinning reserve constraints. The formula for power flow constraints is:

[0030]

[0031] In the formula, is the set of lines in the transmission network, and are the thermal power units, photovoltaic power plants and wind farms connected to the transmission network node i, is the active power of transmission grid node i in period t under scenario s, is the active power of conventional load on transmission grid node i in period t under scenario s, is the active power transmitted by the transmission line ij in time period t under scenario s, θ i,t,s is the voltage phase angle of the transmission grid node i in period t under scenario s, B ij is the susceptance of line ij, and are the upper and lower limits of the voltage phase angle of the transmission network node i, is the maximum transmission capacity of transmission network line ij.

[0032] The formula for thermal power operation constraints is:

[0033]

[0034]

[0035] In the formula, and are the lower and upper limits of the output of thermal power unit k, and are the startup state variable and shutdown state variable of thermal power unit k in period t under scenario s, and are the upper limits of the ramp-down rate and the ramp-up rate of thermal power unit k, and is the minimum start-up time and shutdown time of thermal power unit k.

[0036] The formula for PV operation constraints is:

[0037]

[0038] The formula for wind power operation constraints is:

[0039]

[0040] The formula for pumped storage operation constraints is:

[0041]

[0042] The formula for electrochemical energy storage operation constraints is:

[0043]

[0044]

[0045] The formula for the spinning reserve constraint is:

[0046]

[0047] In the formula, and are the upward and downward rotation reserve capacities of the system in period t under scenario s, r load 、r PV and r WT are the required spinning reserve rates for load, photovoltaic and wind power respectively.

[0048] In view of the deficiency that existing patents and literature have not simultaneously studied the coordinated planning of pumped storage and electrochemical energy storage in new power systems, this paper proposes a method for constructing a two-layer optimization planning model for pumped storage and electrochemical energy storage in power systems. The two-layer optimization planning model constructed according to this method can realize the coordinated planning of pumped storage and electrochemical energy storage, including selecting the locations of pumped storage and electrochemical energy storage, and configuring the power and capacity of pumped storage and electrochemical energy storage.

[0049] Step S3, converting the constructed double-layer nonlinear programming model of the power system pumped storage and electrochemical energy storage into a single-layer linear programming model, specifically includes the following steps:

[0050] Step S301, converting the constructed double-layer optimization planning model of pumped storage and electrochemical energy storage of the power system into a double-layer linear programming model. The specific conversion process is as follows:

[0051] First, convert formula (24) into formula (53), which is as follows:

[0052]

[0053] In the formula, is the number of segments, is the actual output of thermal power unit k in the nth segment interval during period t under scenario s, is the slope of thermal power unit k in the nth segment interval, is the cost of thermal power unit k when it operates at minimum output; is the maximum output of thermal power unit k in the nth segment interval.

[0054] Then, for the product terms of binary variables and continuous variables in formula (25) and formula (33), and After linearization, we can obtain formula (54) and formula (55). The specific linearization calculation formula is as follows:

[0055]

[0056] Finally, for the product terms of binary variables and continuous variables in formula (39) and formula (45), and After linearization, we get formula (56). The specific linearization calculation formula is as follows:

[0057]

[0058] In the formula, M is a positive number.

[0059] Step S302, converting the lower-level operation model in the converted double-level linear programming model into additional constraints of the upper-level programming model, thereby converting the double-level linear programming model into a single-level linear programming model. The specific conversion process is:

[0060] First, construct the Lagrangian function of the underlying running model:

[0061]

[0062] In the formula, f(q u ,q l ) is the objective function of the lower layer running model, b i (q u ,q l) is the inequality constraint of the lower layer running model, d j (q u ,q l ) is the equality constraint of the lower layer running model, q u and q l are the decision variables of the upper planning model and the lower operation model, respectively, i and μ j are the Lagrange multipliers of the inequality constraints and equality constraints of the lower-level running model, respectively. I and J are the numbers of inequality constraints and equality constraints of the lower-level running model, respectively.

[0063] Then, according to the constructed Lagrangian function and Karush-Kuhn-Tucher condition, the lower operating model in the transformed two-level linear programming model is transformed into an additional constraint of the upper-level programming model, thereby transforming the two-level linear programming model into a single-level linear programming model. The calculation formula of the transformed single-level linear programming model is:

[0064] min F(q u ,q l ) (58)

[0065] B(q u ,q l )≤0,D(q u ,q l )=0 (59)

[0066] b(q u ,q l )≤0,d(q u ,q l )=0 (60)

[0067]

[0068] λ i ≥0,i=1,2,…,I (62)

[0069] λ i b i (q u ,q l )=0,i=1,2,...,I ​​(63)

[0070] In the formula, F(q u ,q l ) is the objective function of the upper-level planning model, B(q u ,q l ) is the inequality constraint of the upper-level planning model, D(q u ,q l ) is the equality constraint of the upper-level planning model.

[0071] The present invention proposes to constrain the constructed double-layer optimization planning model according to Karush-Kuhn-Tucher conditions, and realizes efficient solution of the double-layer optimization planning model by converting the double-layer nonlinear optimization model into a single-layer linear optimization model.

[0072] Step S4, solving the converted single-layer linear programming model of pumped storage and electrochemical energy storage by a mathematical solver.

[0073] The present invention also includes a two-layer optimization planning device for power system energy storage, using the method as described above, and the device specifically includes: a pumped storage and electrochemical energy storage operation characteristic model construction module, a power system pumped storage and electrochemical energy storage two-layer nonlinear programming model construction module, and a two-layer nonlinear programming model solving module.

[0074] Among them, the pumped storage and electrochemical energy storage operation characteristic model construction module is used to reflect the operation mechanism and response characteristics of pumped storage and electrochemical energy storage; the power system pumped storage and electrochemical energy storage double-layer nonlinear programming model construction module is used to optimize the site selection and capacity of pumped storage and electrochemical energy storage with the goal of minimizing the annualized total cost at the upper level, and optimize the transmission network operation at the lower level according to the planning scheme with the goal of minimizing the operating cost; the double-layer nonlinear programming model solving module is used to first convert the double-layer nonlinear programming model into a double-layer linear programming model, and then convert the double-layer linear programming model into a single-layer linear programming model.

[0075] The present invention proposes a double-layer optimization planning method and device for energy storage in a power system, which has the following beneficial effects compared with the existing technology:

[0076] This invention proposes a method for pumped storage and electrochemical energy storage operation characteristic model. The operation characteristic model constructed according to this method can fully reflect the operation mechanism and response characteristics of pumped storage and electrochemical energy storage.

[0077] This invention proposes a method for constructing a double-layer optimization planning model for pumped storage and electrochemical energy storage in a power system. The double-layer optimization planning model constructed according to this method can realize the coordinated planning of pumped storage and electrochemical energy storage, including selecting the locations of pumped storage and electrochemical energy storage, and configuring the power and capacity of pumped storage and electrochemical energy storage.

[0078] The present invention proposes to constrain the constructed double-layer optimization planning model according to Karush-Kuhn-Tucher conditions, and realizes efficient solution of the double-layer optimization planning model by converting the double-layer nonlinear optimization model into a single-layer linear optimization model. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1It is a schematic diagram of the process of the present invention;

[0080] Figure 2 is a schematic diagram of the IEEE 24-node system in the present invention;

[0081] Figure 3 It is a schematic diagram of load curves under three scenarios in the present invention;

[0082] Figure 4 It is a schematic diagram of photovoltaic output curves under three scenarios in the present invention;

[0083] Figure 5 It is a schematic diagram of wind power output curves under three scenarios in the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose and features of the present invention more obvious and easy to understand, the present technical solution is described in detail below through embodiments and in conjunction with the accompanying drawings.

[0085] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0086] Embodiment 1:

[0087] like Figure 1 As shown, a two-layer optimization planning method for power system energy storage includes the following steps:

[0088] Step S1, establishing an operation characteristic model of pumped storage and electrochemical energy storage, wherein the formula of the constraint condition of the pumped storage operation characteristic model is:

[0089]

[0090]

[0091] In the formula, and are the power generation and pumping state variables of the pumped storage at node i in period t under scenario s, and are respectively the generating power, pumping power and total power of the pumped storage at node i in period t under scenario s, and are the pumped storage power generation power and pumping power upper limit at node i, respectively. and are the upper and lower storage capacities of pumped storage at node i in period t under scenario s, and are the minimum and maximum storage capacities of the pumped storage reservoir at node i, and are the minimum and maximum storage capacities of the pumped storage reservoir at node i, is the average water-to-electricity conversion coefficient for power generation, is the average water-electricity conversion coefficient of pumping, is the efficiency of pumped storage at node i, and Δt is the time scale.

[0092] In this embodiment, formula (1) is used to indicate that the pumping state and the power generation state are mutually exclusive; formula (2) is used to limit the upper limits of the pumping power and the power generation power; formula (3) is used to indicate the net power of pumped storage; formula (4) is used to indicate the relationship between the upper reservoir capacity and the lower reservoir capacity and the pumping power and the power generation power; formula (5) is used to limit the upper and lower limits of the upper reservoir capacity and the lower reservoir capacity; formula (6) is used to indicate the balance constraint of pumping and power generation within the dispatching day.

[0093] The formula for the constraints of the electrochemical energy storage operation characteristic model is:

[0094]

[0095] In the formula, and are the charging and discharging state variables of the electrochemical energy storage at node i during period t under scenario s, and are the charging power, discharging power and total power of the electrochemical energy storage at node i in time period t under scenario s, and are the upper limits of the charging power and discharging power of the electrochemical energy storage at node i, is the amount of electrochemical energy stored at node i during period t under scenario s, and are the maximum and minimum capacity of electrochemical energy storage at node i, respectively, ES,c and η ES,f They are the charging efficiency and discharging efficiency of electrochemical energy storage, respectively.

[0096] In this embodiment, formula (7) is used to indicate that the charging state and the discharging state are mutually exclusive; formula (8) is used to limit the upper limits of the charging power and the discharging power; formula (9) is used to indicate the net power of the electrochemical energy storage; formula (10) is used to indicate the relationship between the battery capacity and the charging power and the discharging power; and formula (11) is used to limit the upper and lower limits of the battery capacity.

[0097] By establishing the operating characteristic models of pumped storage and electrochemical energy storage, the operating mechanism and response characteristics of pumped storage and electrochemical energy storage can be reflected.

[0098] Step S2, constructing a two-level nonlinear programming model for pumped storage and electrochemical energy storage in the power system. Specifically, the two-level nonlinear programming model includes an upper-level programming model and a lower-level operation model, wherein the objective function of the upper-level programming model is:

[0099] min C all =C inv +C ope (12)

[0100] C inv =C PS +C ES (13)

[0101]

[0102] In the formula, C inv is the investment cost, C ope is the operating cost, minC all To minimize the total annual cost, is the power of pumped storage planned at node i, and are the upper reservoir capacity and lower reservoir capacity of the pumped storage planned at node i, and are the unit power and unit storage capacity investment costs of pumped storage, R PS is the investment recovery factor of pumped storage, and are the power and capacity of the electrochemical energy storage planned at node i, and are the unit power and unit capacity investment costs of electrochemical energy storage, R ES is the electrochemical energy storage investment recovery coefficient, r is the discount rate, τ PS and τ ES They are the lifespan of pumped storage and electrochemical energy storage, respectively.

[0103] The constraints of the upper-level planning model include pumped storage planning constraints and electrochemical energy storage planning constraints, among which,

[0104] The formula for pumped storage planning constraints is:

[0105]

[0106] In the formula, The power upper limit of the pumped storage planned for node i, and are the upper and lower reservoir capacity limits of the pumped storage planned for node i respectively.

[0107] The formula for electrochemical energy storage planning constraints is:

[0108]

[0109]

[0110] In the formula, The power upper limit of the electrochemical energy storage planned for node i, The upper capacity limit of the electrochemical energy storage planned for node i.

[0111] The objective function of the lower-level running model is:

[0112]

[0113] In the formula, minC ope To minimize operating costs, is the fuel cost of thermal power units, is the start-up and shutdown cost of thermal power units, C res Penalty costs for wind and solar curtailment, is the power of thermal power unit k in period t under scenario s, is the operating state variable of thermal power unit k in period t under scenario s, and is the coal cost coefficient of thermal power unit k, is the number of days of scenario s, is the start-up and shutdown cost of thermal power unit k, and are the predicted output and actual output of photovoltaic power station k in time period t under scenario s, and are the predicted output and actual output of wind farm k in period t under scenario s, c PV and c WT are the penalty cost coefficients for abandoning solar power and abandoning wind power respectively.

[0114] The constraints of the lower-level operation model include power flow constraints, thermal power operation constraints, photovoltaic operation constraints, wind power operation constraints, pumped storage operation constraints, electrochemical energy storage operation constraints and spinning reserve constraints. The formula for power flow constraints is:

[0115]

[0116] In the formula, is the set of lines in the transmission network, and are the thermal power units, photovoltaic power plants and wind farms connected to the transmission network node i, is the active power of transmission grid node i in period t under scenario s, is the active power of conventional load on transmission grid node i in period t under scenario s, is the active power transmitted by the transmission line ij in time period t under scenario s, θ i,t,s is the voltage phase angle of the transmission grid node i in period t under scenario s, B ij is the susceptance of line ij, and are the upper and lower limits of the voltage phase angle of the transmission network node i, is the maximum transmission capacity of transmission network line ij.

[0117] The formula for thermal power operation constraints is:

[0118]

[0119] In the formula, and are the lower and upper limits of the output of thermal power unit k, and are the startup state variable and shutdown state variable of thermal power unit k in period t under scenario s, and are the upper limits of the ramp-down rate and the ramp-up rate of thermal power unit k, and is the minimum start-up time and shutdown time of thermal power unit k.

[0120] In this embodiment, formula (32) is used to represent the power constraint of the thermal power unit; formula (33) is used to represent the association between the operating state variables of the thermal power unit and the startup and shutdown state variables; formula (34) is used to represent the ramp constraint of the thermal power unit; formula (35) is used to represent the minimum startup and shutdown time constraints of the thermal power unit.

[0121] The formula for PV operation constraints is:

[0122]

[0123] The formula for wind power operation constraints is:

[0124]

[0125] The formula for pumped storage operation constraints is:

[0126]

[0127]

[0128] In the planning problem, the power and reservoir capacity of the pumped storage are decision variables. Therefore, the pumped storage operation constraints applicable to the planning model can be established based on formulas (1) to (6).

[0129] The formula for electrochemical energy storage operation constraints is:

[0130]

[0131] In the planning problem, the power and capacity of the electrochemical energy storage are decision variables, so the electrochemical energy storage operation constraints suitable for the planning model can be established based on formulas (7) to (11).

[0132] The formula for the spinning reserve constraint is:

[0133]

[0134] In the formula, and are the upward and downward rotation reserve capacities of the system in period t under scenario s, r load 、r PV and r WT are the required spinning reserve rates for load, photovoltaic and wind power respectively.

[0135] In this embodiment, formula (49) is used to represent the upward spinning reserve capacity of the system; formula (50) is used to represent the downward spinning reserve capacity of the system; formula (51) is used to represent that the upward spinning reserve capacity should be greater than the spinning reserve demand; formula (52) is used to represent that the downward spinning reserve capacity should be greater than the spinning reserve demand.

[0136] In this embodiment, the upper planning model optimizes the site selection and capacity of pumped storage and electrochemical energy storage with the goal of minimizing the annualized total cost, and the lower operation model optimizes the operation of the transmission network with the goal of minimizing the operating cost.

[0137] Step S3, converting the constructed double-layer nonlinear programming model of the power system pumped storage and electrochemical energy storage into a single-layer linear programming model, specifically includes the following steps:

[0138] Step S301, converting the constructed two-level optimization planning model of pumped storage and electrochemical energy storage of the power system into a two-level linear programming model. The specific conversion process is as follows:

[0139] First, convert formula (24) into formula (53), which is as follows:

[0140]

[0141] In the formula, is the number of segments, is the actual output of thermal power unit k in the nth segment interval during period t under scenario s, is the slope of thermal power unit k in the nth segment interval, is the cost of thermal power unit k when it operates at minimum output; is the maximum output of thermal power unit k in the nth segment interval.

[0142] Preferably, a piecewise linearization method can be used to transform formula (24) into formula (53).

[0143] Then, for the product terms of binary variables and continuous variables in formula (25) and formula (33), and After linearization, we can obtain formula (54) and formula (55). The specific linearization calculation formula is as follows:

[0144]

[0145] Preferably, the product terms of binary variables and continuous variables in formula (25) and formula (33) can be calculated based on mathematical theory: and Perform linearization.

[0146] Finally, for the product terms of binary variables and continuous variables in formula (39) and formula (45), and After linearization, we get formula (56). The specific linearization calculation formula is as follows:

[0147]

[0148] In the formula, M is a positive number.

[0149] Preferably, the product terms of binary variables and continuous variables in formula (39) and formula (45) can be calculated based on mathematical theory: and Perform linearization.

[0150] Step S302, converting the lower-level operation model in the converted double-level linear programming model into additional constraints of the upper-level programming model, thereby converting the double-level linear programming model into a single-level linear programming model. The specific conversion process is:

[0151] First, construct the Lagrangian function of the underlying running model:

[0152]

[0153] In the formula, f(q u ,q l ) is the objective function of the lower layer running model, b i (q u ,q l ) is the inequality constraint of the lower layer running model, d j (q u ,ql ) is the equality constraint of the lower layer running model, q u and q l are the decision variables of the upper planning model and the lower operation model, respectively, i and μ j are the Lagrange multipliers of the inequality constraints and equality constraints of the lower-level running model, respectively. I and J are the numbers of inequality constraints and equality constraints of the lower-level running model, respectively.

[0154] Then, according to the constructed Lagrangian function and Karush-Kuhn-Tucher condition, the lower operating model in the transformed two-level linear programming model is transformed into an additional constraint of the upper-level programming model, thereby transforming the two-level linear programming model into a single-level linear programming model. The calculation formula of the transformed single-level linear programming model is:

[0155] min F(q u ,q l ) (58)

[0156] B(q u ,q l )≤0,D(q u ,q l )=0 (59)

[0157] b(q u ,q l )≤0,d(q u ,q l )=0 (60)

[0158]

[0159] λ i ≥0,i=1,2,...,I ​​(62)

[0160] λ i b i (q u ,q l )=0,i=1,2,...,I ​​(63)

[0161] In the formula, F(q u ,q l ) is the objective function of the upper-level planning model, B(q u ,q l ) is the inequality constraint of the upper-level planning model, D(q u ,q l ) is the equality constraint of the upper-level planning model.

[0162] Step S4, solving the converted single-layer linear programming model of pumped storage and electrochemical energy storage by a mathematical solver.

[0163] Preferably, the mathematical solver may be a Gurobi solver.

[0164] This embodiment also includes a two-layer optimization planning device for power system energy storage, using the method as described above, and the device specifically includes: a pumped storage and electrochemical energy storage operation characteristic model construction module, a power system pumped storage and electrochemical energy storage two-layer nonlinear programming model construction module, and a two-layer nonlinear programming model solving module.

[0165] Among them, the pumped storage and electrochemical energy storage operation characteristic model construction module is used to reflect the operation mechanism and response characteristics of pumped storage and electrochemical energy storage; the power system pumped storage and electrochemical energy storage double-layer nonlinear programming model construction module is used to optimize the site selection and capacity of pumped storage and electrochemical energy storage with the goal of minimizing the annualized total cost at the upper level, and optimize the transmission network operation at the lower level according to the planning scheme with the goal of minimizing the operating cost; the double-layer nonlinear programming model solving module is used to first convert the double-layer nonlinear programming model into a double-layer linear programming model, and then convert the double-layer linear programming model into a single-layer linear programming model.

[0166] Embodiment 2:

[0167] This embodiment uses Figure 2 The IEEE 24-node system shown in the figure is used as the transmission network topology for the experiment. Specifically, nodes 1, 2, 7, 13, 15, 16, 18, 21 and node 23 in the IEEE 24-node system are connected to the thermal power units respectively. The parameters of the thermal power units are shown in Table 1.

[0168] Table 1

[0169]

[0170] Nodes 3, 9 and 20 are connected to photovoltaic power plants with installed powers of 600MW, 700MW and 500MW respectively, and nodes 6, 12 and 14 are connected to wind farms with installed powers of 400MW, 700MW and 500MW respectively. The load parameters are shown in Table 2.

[0171] Table 2

[0172]

[0173] Pumped storage can be planned at nodes 4 and 7, and electrochemical energy storage can be planned at nodes 16 and 19. The load, photovoltaic and wind power curves in three typical scenarios are as follows: Figure 3 , Figure 4 and Figure 5 shown.

[0174] Tables 3 and 4 show the planning results of pumped storage and electrochemical energy storage, respectively. Table 5 shows the maximum utilization of pumped storage power, upper storage capacity and lower storage capacity under three scenarios, where the maximum utilization refers to the ratio of maximum power / storage capacity to configured power / storage capacity in all periods under the scenario. Table 6 shows the maximum utilization of electrochemical energy storage power and capacity under three scenarios.

[0175] Table 3

[0176]

[0177] Table 4

[0178]

[0179] Table 5

[0180]

[0181] Table 6

[0182]

[0183] As can be seen from Table 3, the proposed method plans pumped storage at nodes 4 and 7, with powers of 206.06MW and 68.04MW respectively; the capacity of the upper and lower reservoirs of the pumped storage at node 4 is 380703 cubic meters, and the capacity of the upper and lower reservoirs of the pumped storage at node 7 is 76493 cubic meters; as can be seen from Table 4, the proposed method plans electrochemical energy storage at nodes 16 and 19, with powers of 114.87MW and 188.85MW respectively; the electrochemical energy storage capacity at node 16 is 229.74MWh, and the electrochemical energy storage capacity at node 19 is 382.84MWh; as can be seen from Table 5, the maximum utilization rates of the power, upper storage capacity and lower storage capacity of the pumped storage at nodes 4 and 7 are all 100%; as can be seen from Table 6, the maximum utilization rates of the power and capacity of the electrochemical energy storage at nodes 16 and 19 are both 100%.

[0184] The experimental results of this embodiment show that the double-layer optimization planning method proposed in the present invention can reasonably configure the power and storage capacity of pumped storage and the power and capacity of electrochemical energy storage.

[0185] This embodiment also sets up two cases, Case 1 and Case 2, for comparison with the method proposed in the present invention, wherein Case 1 only considers the planning of pumped storage, and does not consider the planning of electrochemical energy storage; Case 2 only considers the planning of electrochemical energy storage, and does not consider the planning of pumped storage. Table 7 shows the comparison of the power system cost between the method proposed in the present invention and the two cases; Table 8 shows the comparison of the abandoned wind power and abandoned photovoltaic power between the method proposed in the present invention and the two cases.

[0186] Table 7

[0187]

[0188] Table 8

[0189]

[0190] From the comparison between the method proposed by the present invention and Case 1 in Table 7, it can be seen that the total cost of the power system is reduced by 37.2% compared with Case 1 by using the method proposed by the present invention. From the comparison in Table 8, it can be seen that compared with Case 1, the abandoned photovoltaic power and abandoned wind power are reduced by 94.3% and 97.2% respectively by using the method proposed by the present invention, and the total cost of the power system is reduced by 309.877 million yuan compared with Case 1.

[0191] From the comparison between the method in this paper and Case 2 in Table 7, it can be seen that the total cost of the power system is reduced by 11.7% compared with Case 2 by using the method proposed in this invention. From the comparison in Table 8, it can be seen that compared with Case 2, the abandoned photovoltaic power and abandoned wind power are reduced by 88.3% and 94.3% respectively by using the method proposed in this invention, and the total cost of the power system is reduced by 69.093 million yuan compared with Case 2.

[0192] The experimental results of this embodiment show that the double-layer optimization planning method proposed in the planning method of the present invention not only has less abandoned photovoltaic power and wind power than other cases, but also has lower total power system cost, which proves the effectiveness and efficiency of the method proposed in the present invention.

[0193] It should be noted that the serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0194] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0195] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A two-layer optimization planning method for energy storage in a power system, characterized in that: Includes steps: Step S1, establishing an operation characteristic model of pumped storage and electrochemical energy storage; Step S2, constructing a two-level nonlinear programming model for pumped storage and electrochemical energy storage in the power system; Step S3, converting the constructed double-layer nonlinear programming model of power system pumped storage and electrochemical energy storage into a single-layer linear programming model; Step S4, solving the converted single-layer linear programming model of pumped storage and electrochemical energy storage by a mathematical solver.

2. A two-layer optimization planning method for power system energy storage according to claim 1, characterized in that: The constraint formula of the pumped storage operation characteristic model described in step S1 is: In the formula, and are the power generation and pumping state variables of the pumped storage at node i in period t under scenario s, and are respectively the generating power, pumping power and total power of the pumped storage at node i in period t under scenario s, and are the pumped storage power generation power and pumping power upper limit at node i, respectively. and are the upper and lower storage capacities of pumped storage at node i in period t under scenario s, and are the minimum and maximum storage capacities of the pumped storage reservoir at node i, and are the minimum and maximum storage capacities of the pumped storage reservoir at node i, is the average water-to-electricity conversion coefficient for power generation, is the average water-electricity conversion coefficient of pumping, is the efficiency of pumped storage at node i, and Δt is the time scale.

3. A two-layer optimization planning method for power system energy storage according to claim 1, characterized in that: The constraint formula of the electrochemical energy storage operation characteristic model described in step S1 is: In the formula, and are the charging and discharging state variables of the electrochemical energy storage at node i during period t under scenario s, and are the charging power, discharging power and total power of the electrochemical energy storage at node i in time period t under scenario s, and are the upper limits of the charging power and discharging power of the electrochemical energy storage at node i, is the amount of electrochemical energy stored at node i during period t under scenario s, and are the maximum and minimum capacity of electrochemical energy storage at node i, respectively, ES,c and η ES,f They are the charging efficiency and discharging efficiency of electrochemical energy storage, respectively.

4. A two-layer optimization planning method for power system energy storage according to claim 1, characterized in that: The double-layer nonlinear programming model described in step S2 specifically includes an upper-layer programming model and a lower-layer operation model.

5. A two-layer optimization planning method for power system energy storage according to claim 4, characterized in that: The objective function of the upper-level planning model is: my C all =C inv +C ope (12) C inv =C PS +C ES (13) In the formula, C inv is the investment cost, C ope is the operating cost, minC all To minimize the total annual cost, is the power of pumped storage planned at node i, and are the upper reservoir capacity and lower reservoir capacity of the pumped storage planned at node i, and are the unit power and unit storage capacity investment costs of pumped storage, R PS is the investment recovery factor of pumped storage, and are the power and capacity of the electrochemical energy storage planned on node i, and are the unit power and unit capacity investment costs of electrochemical energy storage, R ES is the electrochemical energy storage investment recovery coefficient, r is the discount rate, τ PS and τ ES They are the lifespan of pumped storage and electrochemical energy storage, respectively. The constraints of the upper-level planning model include pumped storage planning constraints and electrochemical energy storage planning constraints, where: The formula for pumped storage planning constraints is: In the formula, The power upper limit of the pumped storage planned for node i, and are the upper and lower reservoir capacity limits of the pumped storage planned for node i respectively. The formula for electrochemical energy storage planning constraints is: In the formula, The power upper limit of the electrochemical energy storage planned for node i, The upper capacity limit of the electrochemical energy storage planned for node i. The objective function of the lower layer operation model is: In the formula, minC ope To minimize operating costs, is the fuel cost of thermal power units, is the start-up and shutdown cost of thermal power units, C res Penalty costs for wind and solar curtailment, is the power of thermal power unit k in period t under scenario s, is the operating state variable of thermal power unit k in period t under scenario s, and is the coal cost coefficient of thermal power unit k, is the number of days of scenario s, is the start-up and shutdown cost of thermal power unit k, and are the predicted output and actual output of photovoltaic power station k in time period t under scenario s, and are the predicted output and actual output of wind farm k in period t under scenario s, c PV and c WT are the penalty cost coefficients for abandoning solar power and abandoning wind power respectively. The constraints of the lower operation model include power flow constraints, thermal power operation constraints, photovoltaic operation constraints, wind power operation constraints, pumped storage operation constraints, electrochemical energy storage operation constraints and spinning reserve constraints, wherein the formula of power flow constraints is: In the formula, is the set of lines in the transmission network, and are the thermal power units, photovoltaic power plants and wind farms connected to the transmission network node i, is the active power of the transmission grid node i in period t under scenario s, is the active power of conventional load on transmission grid node i in period t under scenario s, is the active power transmitted by the transmission line ij in time period t under scenario s, θ i,t,s is the voltage phase angle of the transmission grid node i in period t under scenario s, B ij is the susceptance of line ij, and are the upper and lower limits of the voltage phase angle of the transmission network node i, is the maximum transmission capacity of transmission network line ij. The formula for thermal power operation constraints is: In the formula, and are the lower and upper limits of the output of thermal power unit k, and are the startup state variable and shutdown state variable of thermal power unit k in period t under scenario s, and are the upper limits of the ramp-down rate and the ramp-up rate of thermal power unit k, and is the minimum start-up time and shutdown time of thermal power unit k. The formula for PV operation constraints is: The formula for wind power operation constraints is: The formula for pumped storage operation constraints is: The formula for electrochemical energy storage operation constraints is: The formula for the spinning reserve constraint is: In the formula, and are the upward and downward rotation reserve capacities of the system in period t under scenario s, r load 、r PV and r WT are the required spinning reserve rates for load, photovoltaic and wind power respectively.

6. A two-layer optimization planning method for power system energy storage according to claim 1, characterized in that: Step S3 includes the steps of: Step S301, converting the constructed power system pumped storage and electrochemical energy storage double-layer optimization planning model into a double-layer linear programming model; Step S302, converting the lower-level operation model in the converted double-level linear programming model into additional constraints of the upper-level programming model, thereby converting the double-level linear programming model into a single-level linear programming model.

7. A two-layer optimization planning method for power system energy storage according to claim 6, characterized in that: The step S301 described in which the constructed two-level optimization planning model of the power system pumped storage and electrochemical energy storage is converted into a two-level linear programming model, the specific conversion process includes the steps of: Step A1, convert formula (24) into formula (53), and formula (53) is specifically as follows: In the formula, is the number of segments, is the actual output of thermal power unit k in the nth segment interval during period t under scenario s, is the slope of thermal power unit k in the nth segment interval, is the cost of thermal power unit k when it operates at minimum output; It is the maximum output of thermal power unit k in the nth segment interval. Step A2: For the product terms of binary variables and continuous variables in formula (25) and formula (33), and After linearization, we can obtain formula (54) and formula (55). The specific linearization calculation formula is as follows: Step A3: For the product terms of binary variables and continuous variables in formula (39) and formula (45), and After linearization, we get formula (56). The specific linearization calculation formula is as follows: In the formula, M is a positive number.

8. A two-layer optimization planning method for power system energy storage according to claim 6, characterized in that: The step S302 converts the lower-level operation model in the converted two-level linear programming model into additional constraints of the upper-level programming model. The specific conversion process includes the following steps: Step B1, construct the Lagrangian function of the lower layer running model: In the formula, f(q u ,q l ) is the objective function of the lower layer running model, b i (q u ,q l ) is the inequality constraint of the lower layer running model, d j (q u ,q l ) is the equality constraint of the lower layer running model, q u and q l are the decision variables of the upper planning model and the lower operation model, respectively, i and μ j are the Lagrange multipliers of the inequality constraints and equality constraints of the lower-level running model, respectively. I and J are the numbers of inequality constraints and equality constraints of the lower-level running model, respectively. Step B2, according to the constructed Lagrangian function and Karush-Kuhn-Tucher condition, the lower operating model in the transformed double-level linear programming model is transformed into an additional constraint of the upper-level programming model, thereby transforming the double-level linear programming model into a single-level linear programming model. The calculation formula of the transformed single-level linear programming model is: min F(q u ,q l ) (58) B(q u ,q l )≤0,D(q u ,q l )=0 (59) b(q u ,q l )≤0,d(q u ,q l )=0 (60) λ i ≥0,i=1,2,...,I (62) λ i b i (q u ,q l )=0,i=1,2,...,I (63) In the formula, F(q u ,q l ) is the objective function of the upper-level planning model, B(q u ,q l ) is the inequality constraint of the upper-level planning model, D(q u ,q l ) is the equality constraint of the upper-level planning model.

9. A double-layer optimization planning device for energy storage in a power system, characterized in that: The device comprises: a pumped storage and electrochemical energy storage operation characteristic model building module, a power system pumped storage and electrochemical energy storage double-layer nonlinear programming model building module, and a double-layer nonlinear programming model solving module. Among them, the pumped storage and electrochemical energy storage operation characteristic model construction module is used to reflect the operation mechanism and response characteristics of pumped storage and electrochemical energy storage; the power system pumped storage and electrochemical energy storage double-layer nonlinear programming model construction module is used to optimize the site selection and capacity of pumped storage and electrochemical energy storage with the goal of minimizing the annualized total cost at the upper level, and optimize the transmission network operation at the lower level according to the planning scheme with the goal of minimizing the operating cost; the double-layer nonlinear programming model solving module is used to first convert the double-layer nonlinear programming model into a double-layer linear programming model, and then convert the double-layer linear programming model into a single-layer linear programming model.

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