A park energy storage multi-objective planning method considering network reconfiguration

Abstract

The application discloses a park energy storage multi-objective planning method considering network reconstruction, relates to the electrical engineering field, and mainly solves the park energy storage planning problem under the access of various distributed resources such as network constraints, distributed energy and distributed energy storage, takes economy as a planning target, considers the reconstruction of a network, network power flow constraints, safety constraints and energy consumption demand, and proposes a park energy storage planning model based on Distflow. In order to facilitate solving, a virtual power is introduced to establish an auxiliary linear equation to represent the switching action of the network. In addition, based on the constraint relationship between the energy consumption demand and the economy demand, the method proposes a relaxation processing method of the multi-objective optimization model. Finally, by using the second-order cone and polyhedral linearization, the non-convex and nonlinear original Distflow network power flow constraint is converted into a linear constraint. After linearization of the network power flow and reconstruction, the model is converted into a mixed integer linear programming model.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering, specifically to a multi-objective planning method for campus energy storage that considers network reconfiguration. Background Technology

[0002] With the increasing awareness of environmental protection in society, the proportion of clean energy generation in the future power system will inevitably gradually increase. On the other hand, with the advancement of my country's power market reform, traditional power consumers such as load aggregators and large industrial loads will be more inclined to allocate resources such as distributed photovoltaic and distributed wind turbines to maximize their trading flexibility in the power market.

[0003] Especially in areas with a certain degree of self-governance, such as industrial parks, commercial parks, and residential areas, the allocation of large-scale distributed power generation resources is an inevitable trend. However, clean energy generators such as photovoltaic and wind turbines are highly susceptible to weather conditions, resulting in significant fluctuations and uncertainties in their power generation. From a physical perspective, this uncertainty and volatility can impact the operation of the power system, particularly during peak load periods, where strong fluctuations in power generation can even lead to dangerous operating conditions. From a market perspective, the uncertainty of clean energy generation can cause significant deviations in market clearing volume, hindering the healthy development of the electricity market. Therefore, considering system security and market stability, without the support of other measures, it is difficult to fully integrate clean energy generation into the grid, leading to resource waste.

[0004] Energy storage devices can effectively mitigate the volatility of clean energy generation. Therefore, energy storage is commonly configured in various industrial parks to improve the efficiency of clean energy utilization. Research on energy storage configuration in industrial parks has been conducted, with the goal of maximizing economic efficiency and considering the time-series linkage between energy storage, clean energy, and park load to enhance the utilization level of clean energy.

[0005] Current research has provided some feasible solutions for energy storage configuration in industrial parks. However, with the development of power distribution networks and the electricity sales market, industrial parks are no longer operating independently; they are often connected by interconnecting lines, enabling the transmission of electricity between them. Furthermore, with the introduction of the "dual carbon" target, industrial park energy storage planning is no longer solely focused on economic efficiency; the benefits of clean energy utilization also need to be given priority.

[0006] However, existing park energy storage planning methods do not take into account network connectivity and power transfer relationships, nor do they consider the constraints between planning economics and energy consumption benefits. Therefore, this invention patent proposes a multi-objective planning method for park energy storage that considers network reconfiguration to solve the above two problems. Summary of the Invention

[0007] In view of the above-mentioned technical shortcomings, the present invention provides a multi-objective planning method for campus energy storage that takes into account network reconfiguration.

[0008] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:

[0009] A multi-objective planning method for campus energy storage considering network reconfiguration includes the following steps:

[0010] Step 1: Considering network power flow constraints and network reconfiguration constraints, propose a park energy storage planning model that includes the optimization objectives of energy consumption benefits and the optimization objectives of planning economic costs;

[0011] Step 2: Relax either the energy consumption efficiency optimization objective or the planning economic cost optimization objective. Use the other optimization objective as the objective function, and substitute the relaxed optimization objective into the objective function to generate a single objective.

[0012] Modeling.

[0013] Step 3: Using second-order cones and polyhedra linearization, the network power flow constraints and network reconstruction constraints are linearized, transforming the single-objective optimization model into a mixed-integer linear programming model;

[0014] Step 4: Solve the mixed-integer linear programming model using a solver to obtain the optimal solution for the multi-objective programming of energy storage in the park.

[0015] Preferably, the energy consumption efficiency optimization target is as follows:

[0016]

[0017] In the formula, g is the index of the photovoltaic device, t is the index of time, and ω is the index of the scene; Ω G Ω T Ω S These represent collections of all photovoltaic devices, all time periods, and all scenarios; P PV P represents the actual power generated by photovoltaic power. Cap β represents the maximum power output of photovoltaics, and β represents the power output coefficient of photovoltaics, which is related to the solar irradiance.

[0018] Preferably, the specific objective for optimizing the economic cost of the plan is as follows:

[0019]

[0020] In the formula, Indicates the amount of energy storage configured at node i; E ESS Indicates the capacity of a unit of energy storage; C E,ESS The cost of batteries for energy storage; C represents the maximum discharge power of a unit energy storage device. P,ESS The cost of the energy conversion system serving energy storage and discharge; Ω ESS The set of all nodes that are selected for energy storage.

[0021] Preferably, in step 2, when the planning economic cost is used as a soft constraint, the optimization objective of the planning economic cost is converted into the following formula:

[0022] Z2≤C max

[0023] In the formula, C max This represents the maximum investment cost of the energy storage system. The relaxed model uses the energy consumption efficiency optimization objective as its objective function.

[0024] Preferably, the network flow constraint is based on Distflow.

[0025] Preferably, the park energy storage planning model proposed in step 1 also considers safety constraints, park coupling node power constraints, photovoltaic constraints, and energy storage constraints.

[0026] Preferably, the security constraints are as follows:

[0027]

[0028] In the formula V i and These are the minimum and maximum voltage levels of node i, respectively; Ω B It is a collection of nodes in the park; V i,t,ω This represents the voltage value at node i in the t-th time period of the ω-th scenario.

[0029] Preferably, in step 2, when energy consumption efficiency is used as a soft constraint, the following constraints are introduced into the park's energy storage planning model:

[0030]

[0031] In the formula, λ is the average light rejection rate. P is the maximum allowable waste rate. Curt Let be the curtailment power of photovoltaic power. The relaxed model uses the planning economic cost optimization objective as the objective function.

[0032] The beneficial effects of this invention are:

[0033] This method primarily addresses the park energy storage planning problem under network constraints and the access of various distributed resources such as distributed energy sources and distributed energy storage. With economic efficiency as the planning objective, it considers network reconfiguration, network power flow constraints, security constraints, and energy consumption requirements, proposing a park energy storage planning model based on Distflow. To facilitate the solution, virtual power is introduced to establish auxiliary linear equations representing the network's switching actions. Furthermore, based on the constraint relationship between energy consumption requirements and economic requirements, this method proposes a relaxation treatment method for the multi-objective optimization model. Finally, using second-order cone and polyhedral linearization, the non-convex and nonlinear original Distflow network power flow constraints are transformed into linear constraints. After linearizing the network power flow and reconfiguration, the established model is transformed into a mixed-integer linear programming model. Attached Figure Description

[0034] Figure 1 A flowchart of a multi-market participation strategy for wind farm energy storage power stations, taking into account the dual uncertainties of power output forecasting and price.

[0035] Figure 2 A schematic diagram illustrating the constraints of a multi-market participation strategy for wind farm energy storage power stations, taking into account the dual uncertainties of power output forecasting and price.

[0036] Figure 3 This diagram illustrates the relaxation process for either the energy consumption benefit optimization objective or the planning economic cost optimization objective in a multi-market participation strategy for wind farm energy storage power stations that takes into account the dual uncertainties of power output forecasting and price.

[0037] Figure 4 A schematic diagram illustrating the transformation of a single-objective optimization model into a mixed-integer linear programming model in a multi-market participation strategy for wind farm energy storage power stations, taking into account the dual uncertainties of power output forecasting and price. Detailed Implementation

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, a multi-objective planning method for campus energy storage considering network reconfiguration includes the following steps:

[0040] Step 1: Considering network power flow constraints and network reconfiguration constraints, propose a park energy storage planning model that includes the optimization objectives of energy consumption benefits and the optimization objectives of planning economic costs;

[0041] Step 2: Relax either the energy consumption efficiency optimization objective or the planning economic cost optimization objective. Use the other optimization objective as the objective function, and substitute the relaxed optimization objective into the objective function to generate a single objective.

[0042] Modeling.

[0043] Step 3: Using second-order cones and polyhedra linearization, the network power flow constraints and network reconstruction constraints are linearized, transforming the single-objective optimization model into a mixed-integer linear programming model;

[0044] Step 4: Solve the mixed-integer linear programming model using a solver to obtain the optimal solution for the multi-objective programming of energy storage in the park.

[0045] Preferably, the energy consumption efficiency optimization target is as follows:

[0046]

[0047] In the formula, g is the index of the photovoltaic device, t is the index of time, and ω is the index of the scene; Ω G Ω T Ω S These represent collections of all photovoltaic devices, all time periods, and all scenarios; P PV P represents the actual power generated by photovoltaic power. Cap β represents the maximum power output of photovoltaics, and β represents the power output coefficient of photovoltaics, which is related to the solar irradiance.

[0048] The above formula defines the photovoltaic power generation efficiency as the ratio between the actual output power and the maximum power that can be generated.

[0049] Preferably, the specific objective for optimizing the economic cost of the plan is as follows:

[0050]

[0051] In the formula, Indicates the amount of energy storage configured at node i; E ESS Indicates the capacity of a unit of energy storage; C E,ESS The cost of batteries for energy storage; C represents the maximum discharge power of a unit energy storage device. P,ESS The cost of the energy conversion system serving energy storage and discharge; Ω ESS The set of all nodes that are selected for energy storage.

[0052] The above formula defines the economic cost of energy storage configuration.

[0053] Preferably, the network flow constraint is based on Distflow.

[0054] Distflow-based campus network power flow representation

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] In the formula, Ω B It is a collection of nodes in the park, Ω Line It is a collection of routes within the park, Ω f (i) represents the set of the starting and ending nodes of a line whose ending node is node i, Ω a (i) represents the set of terminal nodes of a path whose first node is node _i_. ki R ij X ki and X ij The sub-tables represent the resistance and reactance of lines ki and ij; and These are the currents flowing through lines ki and ij, respectively; as well as These are the active power, reactive power, and apparent power flowing through line ij, respectively. and These are the active power and reactive power on line ki, respectively. and These are the active power and reactive power injected by photovoltaics at node i, respectively; η DC Discharge efficiency of energy storage system; P S,DC and P S,C These are the discharge power and charging power of the energy storage system, respectively. These are the active load and reactive load at node i, respectively.

[0061] Equations (3) and (4) represent the balance of active and reactive power at the node, respectively. Equation (5) represents the voltage drop on the line. Equations (6) and (7) are the power constraints of the line.

[0062] like Figure 2 As shown, preferably, the park energy storage planning model proposed in step 1 also considers safety constraints, park coupling node power constraints, photovoltaic constraints, and energy storage constraints.

[0063] Campus coupling node power constraints:

[0064] The purpose of this energy storage plan is to enable the park to achieve full autonomy in energy management and self-sufficiency as much as possible. However, in severe situations such as continuous insufficient sunshine, the park needs to purchase electricity from the upper-level power grid to meet its own load demand. The constraints on the power supply from the upper-level power grid to the park are as follows:

[0065]

[0066]

[0067] In the formula P GSP and These are the minimum and maximum reactive power that the coupled node can deliver, respectively. Q GSP and These are the minimum and maximum active power that the coupled node can transmit, respectively.

[0068] Constraints (8) and (9) restrict the transmission of active and reactive power at the grid-coupled nodes, respectively. When node i is a grid-coupled node, constraints (4) and (5) should be written in the following form:

[0069]

[0070]

[0071] Preferably, the security constraints are as follows:

[0072]

[0073] In the formula V i and These are the minimum and maximum voltage levels of node i, respectively; Ω B It is a collection of nodes in the park; V i,t,ω This represents the voltage value at node i in the t-th time period of the ω-th scenario.

[0074] Photovoltaic constraints:

[0075] The inverter connected to the photovoltaic system can control the power generated by the photovoltaic system and control the phase difference of voltage and current to achieve photovoltaic power factor control. This allows the photovoltaic system to operate under a leading or lagging power factor and to continuously control its own power output.

[0076]

[0077]

[0078]

[0079] In the formula, and Let P represent the maximum and minimum power factors of the g-th photovoltaic cell, respectively. Curt Curtailment of solar power

[0080] Equation (13) defines the power factor range of photovoltaic operation; Equation (14) defines the actual power generated by photovoltaic; Equation (15) defines the range of curtailed power of photovoltaic.

[0081] Reconfiguration constraints:

[0082] When line congestion or safety limits are exceeded in the industrial park, proper network reconfiguration can improve network security and photovoltaic power absorption. For a reconfigurable industrial park, its network status must meet two of the following three conditions:

[0083] C1: The number of lines in use is 1 less than the number of network nodes.

[0084]

[0085] In the formula a ij It is a 0-1 variable that characterizes whether the line is in use.

[0086] C2: There are no isolated islands in the campus network.

[0087] C3: There is no ring network in the park network.

[0088] C1 can be represented by equation (16), but C2 and C3 are difficult to represent directly by mathematical expressions. Therefore, an auxiliary power flow method is used to represent C2.

[0089] Assuming each node in the park has a small virtual active load, and a node is randomly selected in the network as a virtual coupling node, the auxiliary power flow can be characterized as follows:

[0090]

[0091] In the formula and These represent the virtual power flowing on lines ki and ij, respectively.

[0092] The big-M method is used to characterize the on / off state of the line during simulation operation:

[0093]

[0094] In the formula M im Compared to Pim A sufficiently large positive number; The set of lines that participate in the reconstruction.

[0095] Equation (18) indicates that when the line is in operation, the power flowing through the line is unrestricted, and when the line is not in operation, the power of the line is 0.

[0096] For disconnectable circuits, the following auxiliary constraints should be introduced:

[0097]

[0098]

[0099]

[0100]

[0101] Equation (19) represents the voltage relationship between the two ends of the interruptible line. When the line is put into operation, the voltage at both ends is affected by the basic circuit principle of the line. Otherwise, the constraint has no effect. Equations (20) and (21) represent the power relationship on the interruptible line. Equation (22) represents the current relationship on the interruptible line.

[0102] 6) Energy storage constraints

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] In the formula η C It refers to the charging efficiency of the energy storage system; This is the maximum charging power of the energy storage system; A 0-1 variable characterizing whether the energy storage system is in a discharge state; A 0-1 variable characterizing whether the energy storage system is in a charging state; The state of charge of an energy storage device characterizes the energy it stores; and SESS These represent the maximum and minimum states of charge of the energy storage device, M. DC and M C Compared to and A sufficiently large number.

[0112] (23) and (24) indicate that the charging and discharging power of the energy storage device should not exceed its power limit during any time period. (25)-(27) indicate that the energy storage device cannot operate in both charging and discharging states simultaneously. (28)-(29) are constraints on the state of charge of the energy storage device. (30) is a power balance constraint for the energy storage device.

[0113] like Figure 3 As shown, preferably, in step 2, when energy consumption efficiency is used as a soft constraint, the following constraints are introduced into the park's energy storage planning model:

[0114]

[0115] In the formula, λ is the average light rejection rate. P is the maximum allowable waste rate. Curt Let be the curtailment power of photovoltaic power. The relaxed model uses the planning economic cost optimization objective as the objective function.

[0116] in:

[0117]

[0118] Consider relaxing the objective Z1 into a constraint and embedding it into the model, and using Z2 as the optimization objective. The demand for clean energy consumption is usually characterized by the waste rate. In this paper, it is characterized by the curtailment rate. Equation (31) defines the curtailment rate, and Equation (32) constrains the range of the curtailment rate. The relaxed model uses (2) as the objective function and includes (3)-(32) as model constraints.

[0119] like Figure 3 As shown, preferably, in step 2, when the planning economic cost is used as a soft constraint, the optimization objective of the planning economic cost is converted into the following formula:

[0120] Z2≤C max (33)

[0121] In the formula, C max This represents the maximum investment cost of the energy storage system. The relaxed model uses the energy consumption efficiency optimization objective as its objective function.

[0122] When photovoltaic consumption is taken as the objective, the total investment in energy storage in the park is taken as the constraint. Consider how to improve photovoltaic consumption to the optimal level within the investment limit. The relaxed model takes (1) as the objective function and includes (3)-(30) and (33) as model constraints.

[0123] like Figure 4 As shown, the linearization process of the nonlinear nonconvex power flow model in this embodiment of the invention includes:

[0124] The power flow constraints shown in (3)-(7) have strong non-convex nonlinearity, which makes the model difficult to solve directly. Therefore, it is necessary to transform them into linear constraints.

[0125] Introduce auxiliary variables: as well as Constraints (2)-(5), (7), and (12) can be transformed into the following constraints:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Constraints (6) and (37) are still non-convex constraints, and the left side of equation (37) can be transformed into:

[0132]

[0133] Based on second-order cone relaxation, equations (6) and (39) can be transformed into convex constraints:

[0134]

[0135]

[0136] Equations (40) and (41) have the following forms:

[0137]

[0138] Equations (40) and (41) can be transformed using a high-precision polyhedral linearization method:

[0139] τ l ≥|d1|,α l ≥|d2|,l=0 (10)

[0140]

[0141]

[0142] In the formula, τ l and α l It is an auxiliary variable; L is a constant.

[0143] Linear constraints (43)-(45) are equivalent to

[0144] .

Claims

1. A multi-objective planning method for park energy storage considering network reconfiguration, characterized in that, Includes the following steps: Step 1: Considering network power flow constraints and network reconfiguration constraints, propose a park energy storage planning model that includes the optimization objectives of energy consumption benefits and the optimization objectives of planning economic costs; Step 2: Relax either the energy consumption benefit optimization objective or the planning economic cost optimization objective, and use the other optimization objective as the objective function. Substitute the relaxed optimization objective into the objective function to generate a single-objective optimization model. Step 3: Using second-order cones and polyhedra linearization, the network power flow constraints and network reconstruction constraints are linearized, transforming the single-objective optimization model into a mixed-integer linear programming model; Step 4: Solve the mixed-integer linear programming model using a solver to obtain the optimal solution for the multi-objective planning of energy storage in the park; The specific targets for optimizing energy consumption efficiency are as follows: ； In the formula, g is the index of the photovoltaic equipment. t For time indexing, ω For the scene index; Ω G , Ω T , Ω S These are collections of all photovoltaic devices, all time periods, and all scenarios. P PV This refers to the actual power generated by photovoltaics. P Cap The maximum power output of photovoltaics is denoted by , and φ represents the power output coefficient of photovoltaics, which is related to the solar irradiance. The specific objectives for optimizing the economic cost of the plan are as follows: ； In the formula, This indicates the amount of energy storage configured in node i; E ESS Indicates the capacity of a unit of energy storage; The cost of batteries for energy storage; The maximum discharge power of a unit energy storage device; The cost of the energy conversion system serving energy storage and discharge; The set of all candidate nodes configured with energy storage; The network flow constraints are based on Distflow considerations. The park energy storage planning model proposed in step 1 also considers safety constraints, park coupling node power constraints, photovoltaic constraints, and energy storage constraints. The specific security constraints are as follows: ； In the formula and These are the minimum and maximum voltage levels of node i, respectively; It is a collection of nodes in the park; Indicates the first ω The voltage value at node i in time period t under each scenario; In step 2, when energy consumption efficiency is used as a soft constraint, the following constraint is introduced into the park's energy storage planning model: ； In the formula, The average light rejection rate, P Curt The model, after relaxation, uses the curtailment power of photovoltaic power as the objective function, with the goal of optimizing economic cost. Here, g represents the index of the photovoltaic equipment. t For time indexing, ω For the scene index; In step 2, when the planning economic cost is used as a soft constraint, the optimization objective of the planning economic cost is transformed into the following formula: ； In the formula, C max This represents the maximum investment cost of the energy storage system. The relaxed model uses the energy consumption efficiency optimization objective as the target number.

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