Power transmission network extension planning method and device considering optimal consumption of new energy

Abstract

The invention discloses a power transmission network extension planning method and device considering optimal consumption of new energy, and the method comprises the following steps: determining a net load disturbance acceptable domain, and calculating the reliability cost; establishing a power transmission network expansion planning model, and determining an objective function and constraint conditions of the model; processing the power transmission network expansion planning model, and solving the model; wherein the objective function of the model is line investment cost, standby cost, reliability cost and unit operation cost minimization, the quadratic term of the line cost part can be piecewise linearized, and a dual method is adopted to convert a constraint condition uncertainty model into a deterministic model for solving; according to the method, the whole model is converted into a deterministic model, so that the calculation efficiency of the model and the robustness of a planning result are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of power grid expansion planning methods, and in particular to a transmission network expansion planning method and device that considers optimal consumption of new energy. Background Art

[0002] Transmission network expansion planning of power systems is one of their core tasks. A reasonable and efficient expansion planning scheme is not only the key to achieving a balance between power supply and demand, but also ensures the safe and economic operation of the system to meet the future growth demand of load. In the current context of energy shortage and environmental degradation, the penetration rate of renewable energy such as wind power and photovoltaics continues to rise. The randomness, intermittency, volatility and uncontrollability of these energy sources have significantly increased the complexity of transmission network planning. Therefore, improving the grid's ability to accept renewable energy, reasonably allocating system flexibility resources, and coordinating the economy and safety of transmission network planning have become important issues that need to be addressed.

[0003] Traditional planning methods for transmission networks either only consider basic power balance, node voltage, branch capacity and other power flow constraints, and the uncertainties in operation are basically determined by load fluctuations and line failures; or consider power fluctuations under the penetration of renewable energy and configure corresponding frequency and voltage regulation measures. The former planning method is relatively simple, making it unable to flexibly adapt to the access of renewable energy with high intermittency and volatility; the latter often specifies the absorption strategy based on the total renewable energy penetration rate of the system, and cannot make a detailed and reasonable response to the renewable energy disturbance at each point on the grid topology node. If the system cannot respond quickly to the intermittency and volatility due to the access of renewable energy, resulting in insufficient system reliability, the system can only maintain normal power supply by shedding loads and abandoning the output of renewable energy, further affecting the economy and reliability of the system.

[0004] Generally, the modeling methods for transmission network expansion planning with uncertain generation and load are divided into possibility methods, robust optimization and stochastic programming. The possibility method uses membership functions to model uncertain parameters based on expert experience. The robust optimization method is applied to the worst scenarios to ensure stable operation in the most extreme situations faced by the system, but its optimization results are often conservative. Stochastic programming needs to consider the use of massive scenarios to ensure calculation accuracy, and usually adopts improved mathematical optimization algorithms and scenario reduction techniques to solve the problems of long time consumption and heavy calculation burden.

[0005] The above three methods cannot fully achieve a compromise between safety and economy. Therefore, in view of the above problems, it is necessary to study a transmission network expansion planning method based on adaptive robust optimization that considers the optimal absorption of new energy. Summary of the invention

[0006] The existing technology has at least the following shortcomings and deficiencies: 1. The existing transmission network expansion planning scheme does not take into account the fundamental changes in the transmission network brought about by the rapid increase in the penetration rate of new energy. After a high proportion of new energy is connected to the power grid, the planning scheme will not be able to continue to maintain the full absorption of new energy, and the planning idea should shift to the partial absorption of new energy; 2. The existing backup resource configuration method has not yet considered the mismatch between the supply and demand of the new power system transmission network, which will lead to the mismatch between the actual disturbance interval of the system after the decision and the new energy interval that can be absorbed; 3. The existing planning scheme does not reflect the new energy absorption capacity as an optimization object, but only responds to the uncertainty of new energy by adding sufficient reserves in a very conservative way. Although this conservative scheme can give priority to ensuring the stable operation of the system, its economic efficiency is inferior and is not suitable for the future state of high penetration of new energy. In order to solve these problems existing in the prior art, the present invention considers using adaptive robust optimization to overcome them. Adaptive Robust Optimization (Adaptive Robust Optimization) ARO is a method for solving uncertainty optimization problems. Its core idea is to introduce uncertainty parameters into the optimization model and adjust it according to the actual observed data to enhance the robustness of the model. This technology is applied to the transmission network expansion planning. The uncertainty interval of renewable energy power generation can be decomposed into two parts: the internal part and the external part. The internal part can be smoothed by technical adjustments, and the external part can be abandoned with acceptable penalties.

[0007] The present invention proposes a transmission network expansion planning method based on adaptive robust optimization and considering the optimal consumption of new energy. It focuses on exploring planning principles that are more in line with reality, considering a more sophisticated and comprehensive transmission network planning model, so that the planning results are closer to reality and more in line with the development trend of the power system, ensuring the safe and stable operation of the power system and the power supply quality, which has important theoretical significance and practical value.

[0008] The present invention adopts the following technical solutions:

[0009] A transmission network expansion planning method considering optimal consumption of new energy sources comprises the following steps:

[0010] Determine the acceptable domain of net load disturbance and calculate the reliability cost;

[0011] Establish a transmission network expansion planning model and determine the model's objective function and constraints;

[0012] The transmission network expansion planning model is processed and solved; the objective function of the model is to minimize the line investment cost, backup cost, reliability cost, and unit operating cost. The quadratic term of the line cost part can be piecewise linearized, and the dual method is used to transform the constraint uncertainty model into a deterministic model for solution.

[0013] Furthermore, the method for determining the acceptable domain of net load disturbance is:

[0014] The random acceptable net load is expressed by the net load prediction value and the acceptable net load disturbance value, as shown in formula (3):

[0015]

[0016] In the formula, represents the random acceptable active net load on node i in time period t; is the acceptable net load disturbance value, and represents the random acceptable active net load disturbance on node i in time period t.

[0017] The uncertainty of the acceptable net load disturbance is shown in formula (4):

[0018]

[0019] In the formula, and It represents the length of the upward and downward acceptable domains of the net load of node i in time period t.

[0020] Furthermore, the reliability cost is calculated as follows:

[0021] According to the definition of CVaR-NL, the CVaR-NL value of the net load at node i in time period t that exceeds the upper and lower limits of the net load acceptable domain is shown below. The integral of the product of the part of the net load that exceeds the acceptable domain and the corresponding probability density is the CVaR-NL value of the node in this time period:

[0022] CVaR-NL=EENS i,t +EWS i,t

[0023]

[0024] In the formula, and are the lower and upper limits of the active net load disturbance on node i at time t, respectively, and are parameters; and are the lower and upper limits of the net load acceptance capacity of node i at time t, respectively, and are variables; EENS i,t is the expected load shedding amount at node i at time t; EWS i,t is the expected wind curtailment at node i at time t; eens is the penalty cost coefficient of the expected load shedding; π ews is the penalty cost coefficient for the expected wind curtailment; Pr(P i,t ) represents the net load equal to P i,tThe probability of i,t is the net load value of node i at time t.

[0025] Furthermore, the method for establishing the transmission network expansion planning model and determining the objective function of the model is as follows:

[0026] minTC=C inv +C res +C rel +C oper

[0027] Where TC is the total cost; C inv is the line investment cost; C res is the standby cost; C rel is the reliability cost; C oper The unit operating cost;

[0028]

[0029] Where NL is the total number of transmission corridors; ij is the transmission corridor between node i and node j; n ij min is the minimum number of lines between transmission corridors ij; n ij max is the maximum number of lines between transmission corridors ij; c ij is the cost of building a line between transmission corridors ij; α ij k It is a binary variable, indicating whether the kth line is established between ij. If yes, it is 1, otherwise it is 0.

[0030]

[0031] Where d represents the number of typical days in a year; NT represents the number of time periods in a day; NG represents the set of generators; C g up is the cost coefficient of the reserve adjustment of the g-th generator; C g dn is the cost coefficient of the reserve reduction of the g-th generator; R g,t up is the reserve number of the g-th generator at time t; R g,t dn is the reserve number of the g-th generator at time t;

[0032] The above two costs should be variables determined by the first stage of the decision. When using the Benders algorithm, they should be assigned to the main problem. That is, the variables determined by the above two costs do not change during the operation and can cope with all disturbances.

[0033]

[0034] In the formula, a g 、b g 、c g is the operating cost coefficient of the g-th unit, represents the unit output of the g-th unit at time t;

[0035]

[0036] In the formula, π NL is the load shedding penalty factor; π WT is the wind curtailment penalty factor; EENS i,t is the expected load shedding of node i at time t; EWS i,t is the expected wind curtailment amount of node i at time t; AR i,t UB is the upper limit of the safety domain represented by node i at time t, indicating the maximum load fluctuation that can be tolerated; AR i,t LB is the lower limit of the safety domain represented by node i at time t, indicating the maximum acceptable wind power output fluctuation.

[0037] Furthermore, the constraints include:

[0038] Line construction constraints:

[0039]

[0040] Among them, α is a binary variable, indicating whether the kth line is built on the ij transmission corridor; the value of k is {1,2,3,4};

[0041] Node power balance constraints:

[0042]

[0043] In the formula, ΔP i,t D is the load shedding amount at node i at time t; ΔP i,t WT is the abandoned wind volume at node i at time t; P ij,t is the power flow from node i to node j in transmission corridor ij; Ω i G represents the set of generators connected to node i; Ω ij star represents the set of lines with i as the starting node on the transmission corridor ij; Ω ij end represents the set of lines with i as the terminal node on the transmission corridor ij;

[0044] Branch flow constraints:

[0045]

[0046] Where P k ij,t represents the power flow of the kth line between transmission corridors ij at time t; θ i,t represents the phase angle of node i at time t; B ij represents the susceptance of the lines between transmission corridors ij;

[0047]

[0048] P ij max represents the maximum value of the capacity of a single line between transmission corridors ij;

[0049]

[0050] Alternative constraints:

[0051]

[0052] In the formula, R g up_max is the maximum value of the upper spinning reserve that the generator g can retain; R g dn_max is the maximum value of the spinning reserve that generator g can retain;

[0053] Unit output constraints:

[0054]

[0055] Where P g max is the maximum output of generator g; P g min is the minimum value of the generator output g

[0056] Constraints associated with predicted values ​​and disturbance values:

[0057]

[0058] Where P 0 g,t-1 is the output of unit g at the predicted value at time t-1; UR g Indicates the climbing ability of the unit g; DR g Indicates the down-climbing capability of the unit g;

[0059] The relationship between security domain and backup:

[0060]

[0061] In the formula, ΔP i,g,t UB is an auxiliary variable, indicating the part of the upper limit of the safety domain of the i-th node at time t provided by the g-th generator; ΔP i,g,t LB is an auxiliary variable, indicating the part of the lower limit of the safety domain of the i-th node at time t that is provided by the g-th generator;

[0062]

[0063] Safety domain upper limit constraint

[0064]

[0065] Security domain lower limit constraint

[0066]

[0067] Furthermore, the transmission network expansion planning model is processed and the method for solving the model is as follows:

[0068] Simplify the transmission network expansion planning model:

[0069] The objective function is:

[0070]

[0071] In the formula, x is a binary variable; y(u) represents a continuous variable affected by u; u is an uncertain parameter; U is the disturbance range of the uncertain parameter; c T and d T Represent the constant matrices controlling x and y respectively;

[0072] The constraints are:

[0073]

[0074] The above four formulas can summarize all the constraints in the original model, where A, G, T, and Q are the control matrices of each constraint, which are constant coefficient matrices; b, h, and r represent the constant coefficient vectors of the constraints; the first formula represents the construction constraints in the original constraints; the second formula represents the node power balance constraints, the reserve constraints, the unit output constraints, the predicted value and the disturbance value association constraints, and the safety domain and the reserve association constraints; the third formula represents the branch flow constraints; the fourth formula is to isolate the uncertain parameters, I u It represents the isolation matrix that extracts uncertain parameters from many continuous uncertain variables and is composed of 0 and 1;

[0075] Through the above simplified model, we can use the idea of ​​Benders algorithm to fix x to solve binary variables and continuous variables respectively. However, the original model is not the solution format of the standard Benders algorithm, so it needs to be improved. Here, the improvement is applied to the idea of ​​dual problem:

[0076] For a certain x, the original model can be written as follows:

[0077]

[0078] The colon is followed by the dual variables set for the constraints. The above problem can be rewritten as follows:

[0079]

[0080] At the same time, the above formula is also affected by u. By changing u to a variable, a solution model that meets the format can be obtained.

[0081] Finally, after linearization and deterministic transformation, the model is converted into a linear programming model, which can be efficiently solved using mature commercial solvers.

[0082] Furthermore, the linearization of reliability cost in the objective function is:

[0083] Both load disturbance and new energy output distribution conform to normal distribution. Due to its left-right symmetry, therefore:

[0084]

[0085] According to the 3σ principle, we can get

[0086]

[0087] π eens , π ews Respectively represent the risk price of net load exceeding the upper and lower limits of the acceptable domain; the upper net load acceptable domain and downstream payload acceptable range

[0088] Transmission network expansion planning device considering the optimal consumption of new energy, including:

[0089] The net load disturbance acceptable domain and reliability cost calculation module is used to determine the net load disturbance acceptable domain and calculate the reliability cost;

[0090] Model and constraint condition establishment module, used to establish the transmission network expansion planning model and determine the model's objective function and constraint conditions;

[0091] The model solving module is used to process the transmission network expansion planning model and solve the model; the objective function of the model is to minimize the line investment cost, backup cost, reliability cost, and unit operation cost. The quadratic term of the line cost part can be piecewise linearized, and the dual method is used to transform the constraint uncertainty model into a deterministic model for solution.

[0092] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the transmission network expansion planning method that considers the optimal consumption of new energy as described above.

[0093] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the transmission network expansion planning method considering the optimal consumption of new energy as described above is implemented.

[0094] The beneficial technical effects of the present invention are:

[0095] 1. The present invention combines the node admissible domain technology with the reliability cost calculation method, and then transforms it to adapt to the transmission network expansion planning. The improvement is to propose the optimal safety domain applied to the transmission network planning, so that the system can flexibly configure resources instead of accepting excessive new energy output in full; in terms of the model, this improvement constructs a third-layer model of complete operation cost configuration level, which can fix the robust optimization results obtained in the first and second layers in the third-layer model, ensuring that the flexibility resources in the system can be better configured on the basis of system reliability;

[0096] 2. The optimization model proposed by the present invention based on adaptive robust optimization is: the transmission network expansion planning model of the optimal safety domain. The model has a three-layer structure, and the data between the three-layer models influence each other. The first layer is the system line planning, which builds a complete system topology through the configuration of the lines, and reserves sufficient transmission capacity to cope with the impact of uncertainty; the second layer is the backup or safety domain configuration, which reserves sufficient adjustment capabilities for the system through the configuration of the power supply, so that it can maintain system power balance and stable operation when facing the impact of uncertainty; the third layer is the system operation cost configuration, which tries to meet the system uncertainty requirements through rapid adjustment of the generator output and backup calls. When faced with uncertainties that cannot be dealt with, that is, it is necessary to abandon the output of new energy or load, then the system will be punished and restricted;

[0097] 3. The model proposed in this invention is a planning strategy for the region with a large total amount of new energy investment. In the stage where the penetration rate of new energy is large, the use of the optimal absorption model can improve the overall safety and economy of the power system. After the penetration rate of new energy rises to a higher level, the model, as a compromise model, divides uncertainty into two parts, internal and external. The internal part realizes robust optimization to completely absorb uncertainty, and the external part controls the impact of uncertainty through soft penalties. This optimization model can realize the progressive planning of the transmission network and achieve a balance between safety and economy.

[0098] 4. The present invention uses a branch power flow model based on the DC power flow and node power balance. This method is adopted due to the particularity of the grid expansion planning. Since the changes in the grid topology and line impedance cannot be ignored, a new control method for uncertain quantities is established using this method, making the model more applicable.

[0099] 5. The present invention discloses the use of adaptive robust optimization to process uncertainty in the model, and uses the Benders algorithm to decompose the model into a nested three-layer model. The first layer model solves the planning variables, the second layer model solves the safety domain and backup configuration variables, and the third layer solves the system operation variables. The three-layer nested models affect each other, and the entire model is converted into a deterministic model, which greatly improves the calculation efficiency of the model and the robustness of the planning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 Schematic diagram of the flow of the transmission network expansion planning method considering the optimal consumption of new energy in the embodiment of the present invention;

[0101] Figure 2 is the probability density function of the normal distribution in the embodiment of the present invention;

[0102] Figure 3 It is a piecewise linearization diagram of the normal distribution model in an embodiment of the present invention. DETAILED DESCRIPTION

[0103] The following is a further clear and complete description of a method and device for clearing a power capacity market taking into account flexibility provided by the present invention in conjunction with the accompanying drawings:

[0104] Example 1

[0105] A transmission network expansion planning method considering optimal consumption of new energy sources comprises the following steps:

[0106] Determine the acceptable domain of net load disturbance and calculate the reliability cost;

[0107] Establish a transmission network expansion planning model and determine the model's objective function and constraints;

[0108] The transmission network expansion planning model is processed and solved; the objective function of the model is to minimize the line investment cost, backup cost, reliability cost, and unit operating cost. The quadratic term of the line cost part can be piecewise linearized, and the dual method is used to transform the constraint uncertainty model into a deterministic model for solution.

[0109] Specifically, the present disclosure considers the uncertainty of load and renewable energy output, and combines the two together, which is referred to as the uncertainty of net load (i.e., actual load minus renewable energy output);

[0110] Next, we introduce the mathematical model of the net load acceptable domain:

[0111] The fluctuating random net load at each node can be expressed by the predicted value of the net load and the actual disturbance value, as shown in formula (1):

[0112]

[0113] In the formula, represents the random active net load on node i in time period t; represents the predicted value of active net load on node i in time period t; represents the actual disturbance of the random active net load on node i during time period t.

[0114] The uncertainty of net load is described in the form of interval, as shown in formula (2):

[0115]

[0116] In the formula, and They respectively represent the upper and lower limits of the active net load disturbance at node i in time period t, which are determined by the natural disturbance law of actual load and renewable energy output. They are parameters and can be obtained using the probability prediction method.

[0117] Since only line planning is considered in the planning, but the regulation capacity of AGC units is limited, the disturbance that the system can actually accept may be less than the upper and lower limits of the net load disturbance, so the concept of acceptable net load disturbance value is introduced. The acceptable net load disturbance value indicates that the random acceptable net load is not only determined by the actual load and the natural characteristics of renewable energy output, but also limited by the system's acceptance capacity.

[0118] The random acceptable net load is expressed by the net load prediction value and the acceptable net load disturbance value, as shown in formula (3):

[0119]

[0120] In the formula, represents the random acceptable active net load on node i in time period t; is the acceptable net load disturbance value, and represents the random acceptable active net load disturbance on node i in time period t.

[0121] Formula (4) describes the uncertainty of the acceptable net load disturbance. Since the acceptable domain that coincides with the net load disturbance range is effective in smoothing the net load disturbance of the system, the node acceptable domain is not the larger the better. The random acceptable net load disturbance value should not exceed the net load acceptable domain, and should not exceed the upper and lower limits of the net load disturbance.

[0122]

[0123] In the formula, and It represents the length of the upward and downward acceptable domains of the net load of node i in time period t.

[0124] The reliability cost is calculated as:

[0125] Reliability is one of the most important factors to be considered in the planning and operation of power systems. In the current power system, reliability indicators can usually be calculated by two common methods (deterministic method and probabilistic method) to ensure that power generation and transmission facilities are sufficient to meet the power supply needs of the future system. In the power system considering the access of renewable energy, the reliability index is expressed as the penalty price and the expected loss energy, such as the product of expected wind spillage (EWS) / expected energy not supplied (EENS) and the penalty price. This expression is more in line with the physical meaning of the model itself.

[0126] Next, the calculation method of reliability cost is introduced: Assume that the probability density function of the net load on the node is a normal distribution (it can belong to any distribution form in practice). The horizontal axis is the random parameter, and the vertical axis is the probability density function. The two endpoints corresponding to the blue line segment are the maximum upward and downward disturbances of the net load of node i, which are also the upper and lower limits of the acceptable domain, which are parameters; the two endpoints corresponding to the yellow line segment are the upward and downward acceptable domains of the net load of node i, which are decision variables. Any fluctuation of the net load on node i within the range of the yellow line segment will not bring safety risks to the system; if the net load on node i exceeds the upper limit of the acceptable domain, the system cannot accept the excess part, that is, the part corresponding to the shadow on the right side of the figure; if the net load on node i exceeds the lower limit of the acceptable domain, the system cannot accept the excess part, that is Figure 2 The part corresponding to the shadow on the left side.

[0127] According to the definition of CVaR-NL, the CVaR-NL value of the net load at node i in time period t that exceeds the upper and lower limits of the net load acceptable domain is shown below. The integral of the product of the part of the net load that exceeds the acceptable domain and the corresponding probability density is the CVaR-NL value of the node in this time period:

[0128] CVaR-NL=EENS i,t +EWS i,t

[0129]

[0130] In the formula, and are the lower and upper limits of the active net load disturbance on node i at time t, respectively, and are parameters; and are the lower and upper limits of the net load acceptance capacity of node i at time t, respectively, and are variables; EENS i,t is the expected load shedding amount at node i at time t; EWS i,t is the expected wind curtailment at node i at time t; eens is the penalty cost coefficient of the expected load shedding; π ews is the penalty cost coefficient for the expected wind curtailment; Pr(P i,t ) represents the net load equal to P i,t The probability of i,t is the net load value of node i at time t.

[0131] The method for establishing the transmission network expansion planning model and determining the objective function of the model is as follows:

[0132] minTC=C inv +C res +C rel +C oper

[0133] Where TC is the total cost; C inv is the line investment cost; C res is the standby cost; C rel is the reliability cost; C oper The unit operating cost;

[0134]

[0135] Where NL is the total number of transmission corridors; ij is the transmission corridor between node i and node j; n ij min is the minimum number of lines between transmission corridors ij; n ij max is the maximum number of lines between transmission corridors ij; c ijis the cost of building a line between transmission corridors ij; α ij k It is a binary variable, indicating whether the kth line is established between ij. If yes, it is 1, otherwise it is 0.

[0136]

[0137] Where d represents the number of typical days in a year, which is 365 days; NT represents the number of hours in a day, which is 24 hours; NG represents the set of generators; C g up is the cost coefficient of the reserve adjustment of the g-th generator; C g dn is the cost coefficient of the reserve reduction of the g-th generator; R g,t up is the reserve number of the g-th generator at time t; R g,t dn is the reserve number of the g-th generator at time t;

[0138] The above two costs should be variables determined by the first stage of the decision. When using the Benders algorithm, they should be assigned to the main problem. That is, the variables determined by the above two costs do not change during the operation and can cope with all disturbances.

[0139]

[0140] In the formula, a g 、b g 、c g is the operating cost coefficient of the g-th unit, P g G ,t represents the unit output of the g-th unit at time t;

[0141]

[0142] In the formula, π NL is the load shedding penalty factor; π WT is the wind curtailment penalty factor; EENS i,t is the expected load shedding of node i at time t; EWS i,t is the expected wind curtailment amount of node i at time t; AR i,t UB is the upper limit of the safety domain represented by node i at time t, indicating the maximum load fluctuation that can be tolerated; AR i,t LB is the lower limit of the safety domain represented by node i at time t, indicating the maximum acceptable wind power output fluctuation.

[0143] The above two costs should be the variables decided in the second stage, which means that when the network structure and backup configuration results are fixed in the first stage, the generator will call on various resources to respond to disturbances in real time. Disturbances that cannot be dealt with will be softly punished through penalty factors.

[0144] The constraints include:

[0145] Line construction constraints:

[0146]

[0147] Among them, α is a binary variable, indicating whether the kth line is built on the ij transmission corridor; the value of k is {1,2,3,4};

[0148] Node power balance constraints:

[0149]

[0150] In the formula, ΔP i,t D is the load shedding amount at node i at time t; ΔP i,t WT is the abandoned wind volume at node i at time t; P ij,t is the power flow from node i to node j in transmission corridor ij; Ω i G represents the set of generators connected to node i; Ω ij star represents the set of lines with i as the starting node on the transmission corridor ij; Ω ij end represents the set of lines with i as the terminal node on the transmission corridor ij;

[0151] Branch flow constraints:

[0152]

[0153] Where P k ij,t represents the power flow of the kth line between transmission corridors ij at time t; θ i,t represents the phase angle of node i at time t; B ij represents the susceptance of the lines between transmission corridors ij;

[0154]

[0155] P ij max represents the maximum value of the capacity of a single line between transmission corridors ij;

[0156]

[0157] Alternative constraints:

[0158]

[0159] In the formula, R g up _ max is the maximum value of the upper spinning reserve that the generator g can retain; R g dn _ max is the maximum value of the spinning reserve that generator g can retain;

[0160] Unit output constraints:

[0161]

[0162] Where P g max is the maximum output of generator g; P g min is the minimum value of the generator output g

[0163] Constraints associated with predicted values ​​and disturbance values:

[0164]

[0165] Where P 0 g,t-1 is the output of unit g at the predicted value at time t-1; UR g Indicates the climbing ability of the unit g; DR g Indicates the down-climbing capability of the unit g;

[0166] The relationship between security domain and backup:

[0167]

[0168] In the formula, ΔP i,g,t UB is an auxiliary variable, indicating the part of the upper limit of the safety domain of the i-th node at time t provided by the g-th generator; ΔP i,g,t LB is an auxiliary variable, indicating the part of the lower limit of the safety domain of the i-th node at time t that is provided by the g-th generator;

[0169]

[0170] As in the objective function, the upper and lower limits of the safety domain are solved separately, because no matter the load disturbance or the output of renewable energy, wind power abandonment and load shedding are likely to occur only when the load disturbance or renewable energy output is large. Therefore, there is a basis for solving the upper and lower limits of the safety domain separately.

[0171] Safety domain upper limit constraint

[0172]

[0173] What is fixed here is to fix the ratio of the upper limit of the safety domain to the upper limit of the load disturbance and the ratio of the auxiliary variable to the maximum value of the generator output. When the load is maximum, the generator output is limited to be adjusted in the maximum direction.

[0174] Security domain lower limit constraint

[0175]

[0176] What is fixed here is the ratio of the lower limit of the safety domain to the upper limit of wind power output disturbance and the ratio of the auxiliary variable to the minimum value of the generator output. When the wind power generation is maximum, the generator output is limited to be adjusted in the minimum direction.

[0177] The method for processing the transmission network expansion planning model and solving the model is as follows:

[0178] Simplify the transmission network expansion planning model:

[0179] The objective function is:

[0180]

[0181] In the formula, x is a binary variable; y(u) represents a continuous variable affected by u; u is an uncertain parameter; U is the disturbance range of the uncertain parameter; c T and d T Represent the constant matrices controlling x and y respectively;

[0182] The constraints are:

[0183]

[0184] The above four formulas can summarize all the constraints in the original model, where A, G, T, and Q are the control matrices of each constraint, which are constant coefficient matrices; b, h, and r represent the constant coefficient vectors of the constraints; the first formula represents the construction constraints in the original constraints; the second formula represents the node power balance constraints, the reserve constraints, the unit output constraints, the predicted value and the disturbance value association constraints, and the safety domain and the reserve association constraints; the third formula represents the branch flow constraints; the fourth formula is to isolate the uncertain parameters, I u Represents the isolation matrix that extracts uncertain parameters from many continuous uncertain variables, consisting of 0s and 1s.

[0185] Through the above simplified model, we can use the idea of ​​Benders algorithm to fix x to solve binary variables and continuous variables respectively. However, the original model is not in the solution format of the standard Benders algorithm, so it needs to be improved. Here, the improvement is applied to the idea of ​​dual problem.

[0186] For a certain x, the original model can be written as follows:

[0187]

[0188] The colon is followed by the dual variables set for the constraints. Therefore, the above problem can be rewritten as follows:

[0189]

[0190]

[0191] At the same time, the above formula is also affected by u. By variableizing u, a solution model that conforms to the format can be obtained.

[0192] (3) Linearization of reliability cost in the objective function

[0193] This paper believes that both load disturbance and new energy output distribution conform to normal distribution. Due to its left-right symmetry,

[0194]

[0195] According to the 3σ principle, we can get

[0196]

[0197] π eens , π ews Respectively represent the risk price of net load exceeding the upper and lower limits of the acceptable domain; the upper net load acceptable domain and downstream payload acceptable range

[0198] First, piecewise linearize the normal distribution model

[0199] (1) In the horizontal axis, arrive The part is divided into four sections, and the horizontal coordinate O is obtained. s , s=1,2,3,4,5;

[0200] (2) Calculate the horizontal coordinate O s The corresponding ordinate can be obtained through the normal distribution formula, and the obtained results are piecewise linearized; Figure 3 The independent variable between each segment point is x, that is, x here is the load P;

[0201] (3) EENS and EWS are essentially computational expectations, so any segment point can be obtained Approximate value of :

[0202]

[0203] (4) The EENS at the segmented points have been obtained, so the segmented linearization can be used to calculate the EENS value at the net load point:

[0204]

[0205] Therefore, the following three constraints can be introduced to limit the value of x:

[0206]

[0207] Finally, after linearization and deterministic transformation, the model is converted into a linear programming model, which can be efficiently solved using mature commercial solvers.

[0208] The optimization model proposed by the present disclosure based on adaptive robust optimization is: the transmission network expansion planning model of the optimal safety domain. The model is a nested structure. First, the node admissible technology and reliability cost calculation are introduced into the traditional transmission network expansion planning model to construct the transmission network expansion planning model of the optimal safety domain. However, the model has uncertain parameters, binary variables, and continuous variables affected by uncertain parameters, which makes the model unable to be solved directly. Then, the above elements are processed in sequence through adaptive robust optimization to transform the original problem into a deterministic linear problem. Finally, the transmission network expansion planning model of the optimal safety domain is obtained. The model is a three-layer structure. The data between the three-layer models affect each other. The first layer is the system line planning. The complete system topology is constructed through the configuration of the line, and sufficient transmission capacity is reserved to cope with the influence of uncertainty; the second layer is the backup, that is, the safety domain configuration. The power supply configuration reserves sufficient adjustment capabilities for the system to maintain system power balance and stable operation when facing the influence of uncertainty; the third layer is the system operation cost configuration. The system uncertainty requirements are met as much as possible through the rapid adjustment of the generator output and the backup call. When facing the uncertainty that cannot be dealt with, it is necessary to abandon the new energy output or load, and then the system is punished and restricted.

[0209] Example 2

[0210] This embodiment provides a transmission network expansion planning device considering optimal consumption of new energy, including the following:

[0211] The net load disturbance acceptable domain and reliability cost calculation module is used to determine the net load disturbance acceptable domain and calculate the reliability cost;

[0212] Model and constraint condition establishment module, used to establish the transmission network expansion planning model and determine the model's objective function and constraint conditions;

[0213] The model solving module is used to process the transmission network expansion planning model and solve the model; the objective function of the model is to minimize the line investment cost, backup cost, reliability cost, and unit operation cost. The quadratic term of the line cost part can be piecewise linearized, and the dual method is used to transform the constraint uncertainty model into a deterministic model for solution.

[0214] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the transmission network expansion planning method that considers the optimal consumption of new energy as described above.

[0215] Furthermore, the present invention adopts the following technical solutions:

[0216] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the transmission network expansion planning method taking into account the optimal consumption of new energy as described above is implemented.

[0217] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that the facilities of the present invention can be implemented by means of software plus the necessary general hardware platform. The embodiments of the present invention can be implemented using an existing processor, or by a dedicated processor used for this purpose or other purposes for an appropriate system, or by a hard-wired system. The embodiments of the present invention also include a non-transitory computer-readable storage medium, which includes a machine-readable medium for carrying or having a machine-executable instruction or data structure stored thereon; such a machine-readable medium can be any available medium that can be accessed by a general or special-purpose computer or other machine with a processor. For example, such a machine-readable medium can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures, and can be accessed by a general or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hard wiring, or wireless, or a combination of hard wiring and wireless), the connection is also considered a machine-readable medium.

[0218] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A transmission network expansion planning method considering the optimal consumption of new energy sources, characterized in that: The method comprises the following steps: Determine the acceptable domain of net load disturbance and calculate the reliability cost; Establish a transmission network expansion planning model and determine the model's objective function and constraints; The transmission network expansion planning model is processed and solved; the objective function of the model is to minimize the line investment cost, backup cost, reliability cost, and unit operating cost. The quadratic term of the line cost part can be piecewise linearized, and the dual method is used to transform the constraint uncertainty model into a deterministic model for solution.

2. The method for planning the expansion of a power transmission network taking into account the optimal consumption of new energy according to claim 1, characterized in that: The method for determining the acceptable domain of net load disturbance is: The random acceptable net load is expressed by the net load prediction value and the acceptable net load disturbance value, as shown in formula (3): In the formula, represents the random acceptable active net load on node i in time period t; is the acceptable net load disturbance value, and represents the random acceptable active net load disturbance on node i in time period t. The uncertainty of the acceptable net load disturbance is shown in formula (4): In the formula, and It represents the length of the upward and downward acceptable domains of the net load of node i in time period t.

3. The method for planning the expansion of a power transmission network taking into account the optimal consumption of new energy according to claim 1, characterized in that: The reliability cost is calculated as: According to the definition of CVaR-NL, the CVaR-NL value of the net load at node i in time period t that exceeds the upper and lower limits of the net load acceptable domain is shown below. The integral of the product of the part of the net load that exceeds the acceptable domain and the corresponding probability density is the CVaR-NL value of the node in this time period: CVaR-NL=EENS i,t +EWS i,t In the formula, and are the lower and upper limits of the active net load disturbance on node i at time t, respectively, and are parameters; and are the lower and upper limits of the net load acceptance capacity of node i at time t, respectively, and are variables; EENS i,t is the expected load shedding amount at node i at time t; EWS i,t is the expected wind curtailment at node i at time t; eens is the penalty cost coefficient of the expected load shedding; π ews is the penalty cost coefficient for the expected wind curtailment; Pr(P i,t ) represents the net load equal to P i,t The probability of i,t is the net load value of node i at time t.

4. The method for planning the expansion of a power transmission network taking into account the optimal consumption of new energy according to claim 1, characterized in that: The method for establishing the transmission network expansion planning model and determining the objective function of the model is as follows: minTC=C inv +C res +C rel +C oper Where TC is the total cost; C inv is the line investment cost; C res is the standby cost; C rel is the reliability cost; C oper The operating cost of the unit; Where NL is the total number of transmission corridors; ij is the transmission corridor between node i and node j; is the minimum number of lines between transmission corridors ij; is the maximum number of lines between transmission corridors ij; c ij is the cost of building a line between transmission corridors ij; It is a binary variable, indicating whether the kth line is established between ij. If yes, it is 1, otherwise it is 0. Where d represents the number of typical days in a year; NT represents the number of time periods in a day; NG represents the set of generators; is the cost coefficient of increasing the reserve of the g-th generator; is the cost coefficient of the reserve reduction of the g-th generator; is the reserve number of the g-th generator at time t; is the reserve number of the g-th generator at time t; The above two costs should be variables determined by the first stage of the decision. When using the Benders algorithm, they should be assigned to the main problem. That is, the variables determined by the above two costs do not change during the operation and can cope with all disturbances. In the formula, a g 、b g 、c g is the operating cost coefficient of the g-th unit, represents the unit output of the g-th unit at time t; In the formula, π NL is the load shedding penalty factor; π WT is the wind curtailment penalty factor; EENS i,t is the expected load shedding of node i at time t; EWS i,t is the expected wind abandonment amount of node i at time t; is the upper limit of the safety domain represented by node i at time t, indicating the maximum load fluctuation that can be tolerated; is the lower limit of the safety domain represented by node i at time t, indicating the maximum acceptable wind power output fluctuation.

5. The method for planning the expansion of a power transmission network taking into account the optimal consumption of new energy according to claim 1, characterized in that: The constraints include: Line construction constraints: Among them, α is a binary variable, indicating whether the kth line is built on the ij transmission corridor; the value of k is {1,2,3,4}; Node power balance constraints: In the formula, is the load shedding amount of node i at time t; is the abandoned wind volume at node i at time t; P ij,t is the power flow from node i to node j in transmission corridor ij; Ω i G represents the set of generators connected to node i; Ω ij star represents the set of lines with i as the starting node on the transmission corridor ij; Ω ij end represents the set of lines with i as the terminal node on the transmission corridor ij; Branch flow constraints: Where P k ij,t represents the power flow of the kth line between transmission corridors ij at time t; θ i,t represents the phase angle of node i at time t; B ij represents the susceptance of the lines between transmission corridors ij; represents the maximum value of the capacity of a single line between transmission corridors ij; Alternative constraints: In the formula, is the maximum value of the upper spinning reserve that generator g can retain; is the maximum value of the spinning reserve that generator g can retain; Unit output constraints: In the formula, is the maximum value of the generator g output; is the minimum value of the generator output g Constraints associated with predicted values ​​and disturbance values: Where P 0 g,t-1 is the output of unit g at the predicted value at time t-1; UR g Indicates the climbing ability of the unit g; DR g Indicates the down-climbing capability of the unit g; The relationship between security domain and backup: Where ΔP i,g,t UB is an auxiliary variable, indicating the part of the upper limit of the safety domain of the i-th node at time t provided by the g-th generator; ΔP i,g,t LB is an auxiliary variable, indicating the part of the lower limit of the safety domain of the i-th node at time t that is provided by the g-th generator; Safety domain upper limit constraint Security domain lower limit constraint 6. The method for planning the expansion of a power transmission network considering the optimal consumption of new energy according to claim 1, characterized in that: The method for processing the transmission network expansion planning model and solving the model is as follows: Simplify the transmission network expansion planning model: The objective function is: In the formula, x is a binary variable; y(u) represents a continuous variable affected by u; u is an uncertain parameter; U is the disturbance range of the uncertain parameter; c T and d T Represent the constant matrices controlling x and y respectively; The constraints are: The above four formulas can summarize all the constraints in the original model, where A, G, T, and Q are the control matrices of each constraint, which are constant coefficient matrices; b, h, and r represent the constant coefficient vectors of the constraints; the first formula represents the construction constraints in the original constraints; the second formula represents the node power balance constraints, the reserve constraints, the unit output constraints, the predicted value and the disturbance value association constraints, and the safety domain and the reserve association constraints; the third formula represents the branch flow constraints; the fourth formula is to isolate the uncertain parameters, I u It represents the isolation matrix that extracts uncertain parameters from many continuous uncertain variables and is composed of 0 and 1; Through the above simplified model, we can use the idea of ​​Benders algorithm to fix x to solve binary variables and continuous variables respectively. However, the original model is not the solution format of the standard Benders algorithm, so it needs to be improved. Here, the improvement is applied to the idea of ​​dual problem: For a certain x, the original model can be written as follows: The colon is followed by the dual variables set for the constraints. The above problem can be rewritten as follows: At the same time, the above formula is also affected by u. By changing u to a variable, a solution model that meets the format can be obtained. Finally, after linearization and deterministic transformation, the model is converted into a linear programming model and solved using a solver.

7. The method for planning the expansion of a power transmission network taking into account the optimal consumption of new energy according to claim 6, characterized in that: Linearization of reliability cost in the objective function: Both load disturbance and new energy output distribution conform to normal distribution. Due to its left-right symmetry, therefore: According to the 3σ principle, we can get π eens , π ews Respectively represent the risk price of net load exceeding the upper and lower limits of the acceptable domain; Upstream payload acceptable range and downstream payload acceptable range 8. A transmission network expansion planning device considering optimal consumption of new energy, characterized in that: include: The net load disturbance acceptable domain and reliability cost calculation module is used to determine the net load disturbance acceptable domain and calculate the reliability cost; Model and constraint condition establishment module, used to establish the transmission network expansion planning model and determine the model's objective function and constraint conditions; The model solving module is used to process the transmission network expansion planning model and solve the model; the objective function of the model is to minimize the line investment cost, backup cost, reliability cost, and unit operation cost. The quadratic term of the line cost part can be piecewise linearized, and the dual method is used to transform the constraint uncertainty model into a deterministic model for solution.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for planning the expansion of a transmission network taking into account the optimal consumption of new energy as described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the transmission network expansion planning method considering the optimal consumption of new energy as described in any one of claims 1 to claim 7.

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