Generation and transmission expansion planning method for new energy to participate in electric power spot market

Abstract

The invention discloses a power generation and transmission expansion planning method for new energy to participate in an electric power spot market, belongs to the technical field of electrical engineering, and constructs a power generation and transmission expansion planning model based on the new energy to participate in an electric power market environment, and the model is composed of a power generation and transmission expansion planning main problem and an electric power spot market settlement method sub problem; a Benders decomposition method is designed to solve the model, and finally simulation analysis is performed on the model, so that scientificity, effectiveness and feasibility of the model are verified. According to the method, market factors are introduced into the power generation and transmission expansion planning model of the power system, the guiding effect of price signals of the power spot market on power generation and transmission expansion planning is brought into full play, and the influence of different system expected blocking costs on model solving is explored.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and in particular relates to a method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market. Background Art

[0002] At present, the traditional planning method for power generation and transmission expansion is often based on the government's planned source load growth and development trend, but ignores the impact of market mechanisms on planning. In the context of power marketization, the power generation and transmission expansion planning of the power system is no longer a technical issue that is simply decided by the power authorities or power grid companies, but has become a complex decision-making process that requires comprehensive consideration of market factors, economic benefits, technological progress, policies and regulations, and environmental impacts. Among them, the impact of market factors on power system planning is particularly prominent. Therefore, in order to adapt to the new environment of the power spot market with a high proportion of new energy participation, the power generation and transmission expansion planning needs to introduce market factors, eliminate factors that are contrary to market laws, and form a more flexible and efficient planning method.

[0003] In recent years, there have been many studies on the planning technology of power generation and transmission expansion under the power market environment, and certain results have been achieved, including the establishment of a potential value indicator system for evaluating the economy and practicality of planning schemes, and on this basis, a multi-objective transmission network planning model based on potential value indicators and costs; a multi-scenario hybrid planning model for the transmission system under the power market environment considering the impact of faults and the occurrence rate of faults has been proposed. Although the above literature has considered the impact of the power market environment to a certain extent, the research on the impact of power market transactions is not in-depth enough. Other scholars have also proposed a transmission system optimization configuration model that takes into account the game behavior of the main body in the power market, but the current technology only considers the power grid planning, and does not consider the planning situation on the power supply side. Therefore, how to use power trading signals to guide the planning of power generation and transmission expansion while taking into account the investment cost of transmission construction is worthy of in-depth study. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method for planning the expansion of power generation and transmission of new energy sources participating in the electricity spot market, thereby solving the problems in the above-mentioned background technology.

[0005] The object of the present invention is achieved by: a method for planning the expansion of power generation and transmission of new energy participating in the power spot market, comprising the following steps:

[0006] S1. Establish a power generation and transmission planning model;

[0007] S2. Construct the objective function and constraints of power generation and transmission planning;

[0008] S3. Solve the electricity spot market sub-problem.

[0009] Furthermore, the power generation and transmission planning model includes an upper-level main problem model and a lower-level sub-problem model, and the upper-level main problem model is a power generation and transmission expansion planning model, and the lower-level sub-problem model is an electricity spot market clearing model. The upper-level main problem model and the lower-level sub-problem model are set with their own different optimization objectives and constraints. At the same time, the optimization objective function and constraints of the upper-level main problem model are related to the decision variables of the model at this level and the optimal solution of the optimization problem of the lower-level sub-problem model. The optimal solution of the lower-level sub-problem model is also affected by the decision variables of the upper-level main problem model.

[0010] Furthermore, the upper main problem model is:

[0011]

[0012] The lower level sub-problem model is:

[0013]

[0014] Where x and y are the decision variables of the upper and lower models, respectively; F and f are the optimization objectives of the upper and lower models, respectively; and H and h are the related constraints of the upper and lower models, respectively.

[0015] Furthermore, the objective function of the power generation and transmission planning includes the objective function of the power generation and transmission expansion planning model, and the objective function of the power generation and transmission expansion planning model is to minimize the total cost of the power system, and specifically includes the power system investment cost and the power system operation cost. The calculation formulas for the investment cost and the operation cost are:

[0016] min C all =C invest +C op

[0017]

[0018] Where, Ω G ,Ω L ,Ω W and Ω PV are the set of conventional units, the set of transmission lines, the set of wind turbines and the set of photovoltaic generators; C all is the total investment and operating costs; C invest and C op They are the sum of the system's investment costs and operating costs respectively; and are the investment costs of traditional generator set g, transmission line l, wind turbine set w and photovoltaic power station pv respectively; and They are all Boolean variables, representing the construction status of conventional units g, transmission lines l, wind turbines w, and photovoltaic power stations pv, respectively. 0 means not in construction, and 1 means in construction. is the operating cost of the traditional unit g; and are the power abandonment penalty costs of wind turbine w and photovoltaic power station pv, and They are the output of the conventional unit g, the actual output of the wind turbine unit w and the actual output of the photovoltaic power station pv at time t respectively; and They are the predicted output of wind turbine w and photovoltaic power station pv respectively.

[0019] Furthermore, the constraints of power generation and transmission planning include decision variable constraints, node power balance constraints, line flow constraints, conventional unit output constraints, new energy unit output constraints and node phase angle constraints;

[0020] The decision variable constraints are:

[0021]

[0022] Where: and They are the set of conventional units that have been put into operation, the set of transmission lines, the set of wind farms and the set of photovoltaic power stations, which constrain the situation of the traditional units, transmission lines and new energy units that have been put into operation in the system;

[0023] The node power balance constraint is:

[0024]

[0025] Where: is the active power transmitted by transmission line l; is the set of traditional units located at node i; Ω B is a set of nodes, s(l) and r(l) are the sending and receiving ends of line l respectively, p i,t is the load forecast value of node i at time t;

[0026] The line power flow constraint is:

[0027]

[0028] Where: is the maximum active transmission power of transmission line l; is the reactance of transmission line l; δ s(l),t and δ r(l),t is the phase angle value between the sending end and the receiving end of the transmission line l;

[0029] The conventional unit output constraint is:

[0030]

[0031] Where: is the installed capacity of conventional unit g;

[0032] The output constraints of the new energy units are:

[0033]

[0034] Where: is the predicted output of wind turbine w at time t; is the predicted output of the photovoltaic power station pv at time t;

[0035] The node phase angle constraint is:

[0036]

[0037] Where: is the reference node set, δ max is the maximum phase angle value of the node; δ i,t is the voltage phase angle of node i in year t.

[0038] Furthermore, solving the electricity spot market sub-problem includes clearing the spot market of the upper-level power grid planning results based on time-series production simulation technology, calculating the system congestion cost, and returning it to the upper-level planning model to optimize the transmission network planning results.

[0039] Furthermore, in order to ensure that the subproblem always has a solution, an auxiliary variable s is introduced into the subproblem l,t , to characterize the line congestion in the sub-problem. The objective function of the safety constraint unit combination in the sub-problem includes the winning bid price of conventional units and new energy units, the start-up and shutdown cost of conventional units, and the system congestion cost, which is expressed as:

[0040] min C G +C W +C PV +C SU +C SD +C cong

[0041]

[0042] Where: C G is the winning bid price of conventional units, C W is the winning bid price of the wind turbine, C PV is the winning bid price of the solar power unit, C SU is the startup cost of conventional units, C SDis the downtime cost of conventional units, is the blocking penalty cost, s l,t is an auxiliary variable, indicating the line blocking situation in the sub-problem;

[0043] Since the upper-level planning main problem has obtained the new planning grid of the system, the upper and lower limit constraints of the unit output are expressed as:

[0044]

[0045] Where: and They are the planning results of conventional units g, wind turbines w and photovoltaic power stations pv obtained by solving the power generation and transmission expansion planning model of the node; are the power output of conventional unit g, wind turbine unit w and photovoltaic power station pv at time t respectively; is a Boolean variable, indicating the start and stop status of the conventional unit g at time t; Indicates that the unit is in the starting state. Indicates that the unit is in shutdown state;

[0046] Similarly, the system power balance constraint can be modified as follows:

[0047]

[0048] Where: P t * is the system load value at time t, which can be obtained from the transmission expansion planning model;

[0049] The transmission line power flow constraint can be modified as follows:

[0050]

[0051] Where: To solve the power generation and transmission expansion planning model, we need to obtain the maximum transmission capacity and Ω of the transmission line l. B is the node set, G l,i The generator output power transfer distribution factor for the node i where the generator set is located on the line l.

[0052] Furthermore, the system reserve constraint is,

[0053]

[0054] Where: u g,t is a Boolean variable, indicating the start and stop status of the conventional unit g at time t, u g,t =1 means the unit is in the starting state, u g,t =0 means the unit is in shutdown state; and are the minimum technical output and maximum technical output of conventional unit g respectively; and They are the upper and lower reserve requirements of the system. In the actual operation of the power system, load fluctuations and unexpected failures of power equipment often occur. The power system uses reserve constraints to deal with these randomness to ensure the safety and reliability of power supply;

[0055] The unit climbing capability constraint is:

[0056]

[0057] Where: and are the up-climbing capacity and down-climbing capacity of the unit in one time step, respectively. M is a very large positive integer, which is used to ensure the stable operation of the power system. The output change of the conventional unit in a continuous period must comply with the limit of its climbing capacity.

[0058] Minimum start and stop time constraints,

[0059]

[0060] Where: is the time that the conventional unit g still needs to continue to operate at time t; is the time that the conventional unit g still needs to be shut down at time t; is the minimum continuous startup time of the conventional unit g, is the minimum continuous downtime of conventional unit g;

[0061] The objective function of the safety-constrained economic dispatch in the sub-problem reduces the start-up and shutdown costs of conventional units. The objective function of the safety-constrained economic dispatch in the sub-problem is:

[0062] min C G +C W +C PV +C SU +C SD +C cong

[0063] The constraints of the security-constrained economic dispatch in the subproblem, the system congestion price component can be the dual value generated by the transmission line flow constraint and It is expressed as:

[0064]

[0065] Beneficial effects of the present invention: By using the electricity spot market clearing model as a sub-problem of the two-level programming model, we can obtain: the objective function, constraints and decision variables of the main problem of power transmission and generation expansion planning and the optimal solution of the electricity spot market clearing optimization problem are related, and the optimal solution of the electricity spot market clearing sub-problem is also affected by the decision variables of the main problem of power transmission and generation expansion planning. This means that we have successfully introduced market factors into the power system power generation and transmission expansion planning. After further solving the model, we can determine whether the guiding role of the price signal of the electricity spot market is reflected, and at the same time, we can analyze the impact of different system expected congestion costs on the model solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart for solving the power generation and transmission expansion planning in the power spot market environment of the present invention;

[0067] Figure 2 It is a grid topology diagram of the present invention;

[0068] Figure 3 It is a comparison chart of system planning results in different scenarios of the present invention. DETAILED DESCRIPTION

[0069] In order to more clearly illustrate and explain the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings.

[0070] This embodiment discloses a method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market;

[0071] First, a power generation and transmission expansion planning model under the environment of new energy participating in the electricity spot market is established. This model is a two-level planning model. The upper main problem is the power generation and transmission expansion planning model, and the lower sub-problem is the electricity spot market clearing model. The two-level planning problem has the characteristics of hierarchy, independence, conflict, priority and autonomy. The upper main problem and the lower sub-problem have their own different optimization objectives and constraints. At the same time, the optimization objective function and constraints of the upper main problem are related to the decision variables of the model at this level and the optimal solution of the optimization problem of the lower sub-problem, and the optimal solution of the lower sub-problem is also affected by the decision variables of the upper main problem. The mathematical model of the two-level planning problem is as follows:

[0072]

[0073] The above formula corresponds to the upper-level planning model, and the following formula corresponds to the lower-level model; x and y are the decision variables of the upper and lower-level models, respectively; F and f are the optimization objectives of the upper and lower-level models, respectively; and H and h are the relevant constraints of the upper and lower-level models, respectively.

[0074] Main issues in power generation and transmission expansion planning

[0075] (1) Objective function

[0076] The objective function of the power generation and transmission expansion planning model is usually to minimize the total cost of the power system, which specifically includes the following two parts: the power system investment cost and the power system operation cost, as shown in (1-1):

[0077] min C all =C invest +C op (1-1)

[0078]

[0079] Where: Ω G ,Ω L ,Ω W and Ω PV are the set of conventional units, the set of transmission lines, the set of wind turbines and the set of photovoltaic generators; C all is the total investment and operating costs; C invest and C op They are the sum of the system's investment costs and operating costs respectively; and are the investment costs of traditional generator set g, transmission line l, wind turbine set w and photovoltaic power station pv respectively; and They are all Boolean variables, representing the construction status of conventional units g, transmission lines l, wind turbines w, and photovoltaic power stations pv, respectively. 0 means not in construction, and 1 means in construction. is the operating cost of the traditional unit g; and are the power abandonment penalty costs of wind turbine w and photovoltaic power station pv respectively. and They are the output of the conventional unit g, the actual output of the wind turbine unit w and the actual output of the photovoltaic power station pv at time t respectively; and They are the predicted output of wind turbine w and the predicted output of photovoltaic power station pv. Formula (1-2) and formula (1-3) are the calculation formulas for investment cost and operation cost respectively.

[0080] (2) Constraints

[0081] 1) Decision variable constraints:

[0082]

[0083] Where: and They are the set of conventional units, transmission lines, wind farms and photovoltaic power stations that have been put into operation. Formulas (1-4) to (1-7) constrain the conditions of the traditional units, transmission lines and new energy units that have been put into operation in the system.

[0084] 2) Node power balance constraints:

[0085]

[0086] Where: is the active power transmitted by transmission line l; is the set of traditional units located at node i; Ω B is a set of nodes, s(l) and r(l) are the sending and receiving ends of line l respectively, p i,t is the load forecast value of node i at time t. Formula (1-8) represents the node power balance equation.

[0087] 3) Line flow constraints:

[0088]

[0089] Where: is the maximum active transmission power of transmission line l; is the reactance of transmission line l; δ s(l),t and δ r(l),t is the phase angle value between the sending end and the receiving end of the transmission line l. Formula (1-9) and Formula (1-10) are the upper and lower limit constraints of the transmission power of line l and the calculation formula of the line power flow respectively.

[0090] 4) Output constraints of conventional units:

[0091]

[0092] Where: is the installed capacity of conventional unit g.

[0093] 5) Output constraints of new energy units:

[0094]

[0095] Where: is the predicted output of wind turbine w at time t; is the predicted output of the photovoltaic power station pv at time t;

[0096] 6) Node phase angle constraint:

[0097] -δ max ≤δ i,t ≤δ max ,i∈Ω B ,t∈Ω T(1-14)

[0098]

[0099] Where: is the reference node set, δ max is the maximum phase angle value of the node; δ i,t is the voltage phase angle of node i in year t. Formula (1-14) is the upper and lower limit constraints of the node phase angle; Formula (1-15) indicates that the reference node voltage phase angle is 0.

[0100] The clearing subproblem of the electricity spot market

[0101] After the initial planning scheme is obtained for the main problem of power generation and transmission expansion planning, the power spot market sub-problem will continue to be solved. The power spot market clearing model is based on time-series production simulation technology. It clears the upper-level power grid planning results in the spot market, calculates the system congestion cost, and returns it to the upper-level planning model to further optimize the transmission network planning results. In order to ensure that the sub-problem always has a solution, an auxiliary variable s is introduced in the sub-problem l,t , to characterize the line congestion in the sub-problem. The objective function of the safety constraint unit combination in the sub-problem includes the winning bid price of conventional units and new energy units, the start-up and shutdown costs of conventional units, and the system congestion cost, which can be expressed as:

[0102] min C G +C W +C PV +C SU +C SD +C cong (1-16)

[0103]

[0104] Where: C G is the winning bid price of conventional units, C W is the winning bid price of the wind turbine, C PV is the winning bid price of the solar power unit, C SU is the startup cost of conventional units, C SD is the downtime cost of conventional units, is the blocking penalty cost, s l,t is an auxiliary variable, indicating the line blocking situation in the sub-problem.

[0105] Since the upper-level planning main problem has obtained the new planning grid of the system, the upper and lower limit constraints of the unit output can be expressed as:

[0106]

[0107] Where: and They are the planning results of conventional units g, wind turbines w and photovoltaic power stations pv obtained by solving the power generation and transmission expansion planning model in Section 1.1.1 respectively. are the power output of conventional unit g, wind turbine unit w and photovoltaic power station pv at time t respectively; is a Boolean variable, indicating the start and stop status of the conventional unit g at time t ( Indicates that the unit is in the starting state. Indicates that the unit is in shutdown state).

[0108] Similarly, the system power balance constraint can be modified as follows:

[0109]

[0110] Where: P t * is the system load value at time t, and its value can be obtained from the transmission expansion planning model.

[0111] The transmission line power flow constraint can be modified as follows:

[0112]

[0113] Where: To solve the power generation and transmission expansion planning model, we need to obtain the maximum transmission capacity and Ω of the transmission line l. B is the node set, G l,i The generator output power transfer distribution factor for the node i where the generator set is located on the line l.

[0114] In summary, the constraints of the safety constraint unit combination in the sub-problem can be expressed as:

[0115]

[0116] Formulas (1-24) and (1-25) represent the system reserve constraints, and (1-26)-(1-33) represent the unit climbing capability constraints as follows:

[0117] 1) System backup constraints

[0118]

[0119] Where: u g,t is a Boolean variable, indicating the start and stop status of the conventional unit g at time t, u g,t =1 means the unit is in the starting state, u g,t =0 means the unit is in shutdown state; and are the minimum technical output and maximum technical output of conventional unit g respectively; and They are the upper reserve and lower reserve requirements of the system respectively. In the actual operation of the power system, unforeseen situations such as load fluctuations and unexpected failures of power equipment often occur. The power system responds to these randomness through reserve constraints to ensure the safety and reliability of power supply. Formula (1-24) is the system positive spinning reserve constraint; Formula (1-25) is the system negative spinning reserve constraint.

[0120] 3) Unit climbing capability constraints

[0121]

[0122] Where: and are the up-climbing capacity and down-climbing capacity of the unit in one time step, respectively, and M is a very large positive integer. Equations (1-26) and (1-27) are the up-climbing constraint and down-climbing constraint of the unit, respectively, indicating that in order to ensure the smooth operation of the power system, the output change of the conventional unit in a continuous period must comply with the limit of its climbing capacity.

[0123] 4) Minimum start and stop time constraints

[0124]

[0125] Where: is the time that the conventional unit g still needs to continue to operate at time t; is the time that the conventional unit g still needs to continue to be shut down at time t. is the minimum continuous startup time of the conventional unit g, is the minimum continuous downtime of conventional unit g.

[0126] Compared with formula (1-16), the objective function of safety-constrained economic dispatch in the sub-problem reduces the start-up and shutdown cost of conventional units. The objective function of safety-constrained economic dispatch in the sub-problem is:

[0127] min C G +C W +C PV +C SU +C SD +C cong (1-34)

[0128] The constraints of the security-constrained economic dispatch in the sub-problem are given by equations (1-24)-(1-25) and (1-18)-(1-22). The system congestion price component can be the dual value generated by the transmission line flow constraint and It is expressed as shown in formula (1-35)-formula (1-36):

[0129]

[0130] In summary, by adding the electricity spot market clearing model as a sub-problem of the bi-level programming model, we can obtain that the objective function, constraints and decision variables of the main problem of transmission and generation expansion planning are related to the optimal solution of the electricity spot market clearing optimization problem, and the optimal solution of the electricity spot market clearing sub-problem is also affected by the decision variables of the main problem of transmission and generation expansion planning. This means that we have successfully introduced market factors into the power system transmission and generation expansion planning. After further solving the model, we can determine whether the guiding role of the price signal of the electricity spot market is reflected, and we can also analyze the impact of different system expected congestion costs on the model solution.

[0131] Solution Algorithm Flow

[0132] The main problem model of power generation and transmission expansion planning and the sub-problem model of power spot market clearing are both mixed integer linear programming models. The solution flow chart of power generation and transmission expansion planning in the proposed power market environment is shown in Figure 1.

[0133] The specific solution steps are as follows:

[0134] Step 1: Solve the generation and transmission expansion planning model.

[0135] Step 2: Let k=1.

[0136] Step 3: Solve the electricity spot market clearing subproblem model. If sl,t=0 is satisfied, go to step 4; otherwise, generate a feasible cut constraint.

[0137] Step 4: Determine whether a new feasible cut is generated. If so, feed the feasible cut back to the generation and transmission expansion planning model based on opportunity constraints and jump to step 1. Otherwise, the solution process ends.

[0138] Case Analysis

[0139] To verify the effectiveness of the proposed model, simulations were performed based on the grid topology of a provincial power grid of 500 kV and above in East China and the historical data of renewable energy output and load in the region. The proposed models are all mixed integer linear programming models, so the CPLEX solver is used to solve them.

[0140] Case Study

[0141] The topological structure of a provincial power grid with a voltage of 500 kV or above in East China is as follows: Figure 2As shown. The upper limits of wind power installed capacity at nodes 3, 4, 10, and 11 of the system are 3200MW, 6400MW, 11200MW, and 11200MW respectively. The upper limits of photovoltaic installed capacity at nodes 1, 3, 4, 10, and 13 of the system are 9000MW, 7200MW, 7200MW, 6300MW, and 6300MW respectively. The maximum load of the system is 130722.2MW, and the installed capacity of conventional units is 104811.0MW. This section assumes that the investment cost of all new transmission lines is 1.4 million yuan / km; the investment cost of all photovoltaic power stations is 2 million yuan / MW; the investment cost of wind turbines is 2.5 million yuan / MW; the investment cost of coal-fired units is 1.5 million yuan / MW; and the investment cost of gas-fired units is 800,000 yuan / MW.

[0142] K-means clustering was used to analyze the historical data of wind and solar power output and load for 8760 hours throughout the year in the region, and the clustering results were obtained. This method fully considers the temporal sequence and correlation of wind and solar power output and load in the region, and also shows the characteristics of wind and solar power output variation within the day. Then, by calculating the Pearson correlation coefficient, the class with the highest correlation with the overall data was selected as the typical daily curve of wind power output, photovoltaic output and load, and the planning model was solved based on this.

[0143] Results Analysis

[0144] In order to fully analyze the impact of the electricity spot market environment with a high proportion of renewable energy participation on the results of power generation and transmission expansion planning, 100% of renewable energy is selected to participate in the market by quotation, and the following three scenarios are designed and verified by example simulation.

[0145] Scenario 1: Without considering the electricity spot market environment, only the power generation and transmission expansion planning model is considered for solution;

[0146] Scenario 2: Considering the lower expected system congestion cost, the generation and transmission expansion planning model based on the power spot market environment is solved;

[0147] Scenario 3: Considering moderate system expected congestion cost, the power generation and transmission expansion planning model based on the electricity spot market environment is solved.

[0148] (1) Consider only the power generation and transmission expansion plan

[0149] Without considering the electricity spot market environment, the constructed model is solved, and the transmission line planning results are shown in Table 1, the conventional unit planning results are shown in Table 2, and the new energy unit planning results are shown in Table 3, which are used to compare the subsequent planning results considering the electricity spot market environment. It can be seen from the above two tables that without considering the electricity spot market environment, the power generation and transmission expansion planning results are 5 new lines, with a total investment of 552.44 million yuan in transmission lines; a total of 11,500 MW of new conventional units and 8,736 MW of new energy units, with a total investment of 35.672 billion yuan. Without considering the market environment, the investment costs of transmission lines and various units in the power system are relatively low, and the planned power system can only meet the needs of power supply and transmission.

[0150] Table 1 Line planning results in traditional power generation and transmission expansion planning

[0151] New line planning results Number of new lines Cost of new line (10,000 yuan) <![CDATA[l 1-3 ]]> 1 5950 <![CDATA[l 2-5 ]]> 2 20692 <![CDATA[l 2-7 ]]> 1 18690 <![CDATA[l 5-6 ]]> 1 9912 total 5 55244

[0152] Table 2 Results of conventional unit planning in traditional power generation and transmission expansion planning

[0153]

[0154] Table 3 Results of planning for new energy units in traditional power generation and transmission expansion planning

[0155]

[0156]

[0157] In the planning results without considering the power spot market environment, the clearing prices of each node in the system can be seen from the table. In the planning results without considering the market environment, the clearing prices of each node in the system are quite different. The highest clearing price in the system is 321.56 yuan / MWh, the lowest clearing price is 267.36 yuan / MWh, and the difference between the highest and lowest clearing prices is 54.2 yuan / MWh. This shows that the congestion in the system is serious at this time, and the power spot market has not played a good price-guiding role.

[0158] In the planning results without considering the electricity spot market environment, the clearing prices of different entities in the market can be seen from the table. The clearing price of gas-fired units is the highest, up to 335.71 yuan / MWh, and its clearing price is slightly higher than the clearing price on the load side. The main reason is that most gas-fired units are used as peak-shaving power sources; while wind turbines and photovoltaic power stations benefit from their extremely low marginal costs, and the clearing prices of both are much lower than the clearing prices on the load side. Among them, the clearing price of wind turbines is the lowest, only 277.76 yuan / MWh. Compared with wind turbines, photovoltaic power stations are more often built in areas close to load centers. Therefore, their clearing prices are slightly higher than those of wind turbines.

[0159] Table 4 System node clearing electricity prices in traditional power generation and transmission expansion planning

[0160] node Node electricity price (yuan / MWh) node Node electricity price (yuan / MWh) 1 313.73 8 297.54 2 317.45 9 285.22 3 312.57 10 288.25 4 301.26 11 321.56 5 320.38 12 297.54 6 298.75 13 285.22 7 274.8

[0161] Table 5 Clearing prices of different market players under the traditional power generation and transmission expansion plan

[0162]

[0163]

[0164] (2) Grid planning results in the electricity spot market environment considering lower system expected congestion costs

[0165] In the electricity spot market environment, by setting a lower system expected congestion cost, the price-oriented role of the electricity spot market is maximized, the system node electricity price is balanced as much as possible, and the constructed model is solved to obtain the line planning results, conventional unit planning results, and new energy unit planning results. It can be observed from the results in the table that in order to ensure the balance of electricity prices at each node in the system as much as possible and give full play to the price-oriented role of the electricity spot market, 11 new transmission lines were built in the system at this time, and the total investment cost of the transmission lines was 1550.64 million yuan, which increased by 180.68% compared with the total investment cost of the transmission lines when the electricity spot market environment was not considered; the new conventional units totaled 6000MW, the new energy units totaled 22890MW, and the total investment of the units was 53.905 billion yuan. Considering the market environment, the line investment cost and the unit investment cost have increased significantly compared with the planning results without considering the electricity spot market. The power system has exchanged a higher investment cost for the balance of the clearing electricity prices at each node in the system.

[0166] Table 6 Route planning results considering lower system expected congestion cost

[0167] New line planning results Number of new lines Cost of new line (10,000 yuan) <![CDATA[l 1-3 ]]> 1 5950 <![CDATA[l 2-5 ]]> 3 31038 <![CDATA[l 2-7 ]]> 2 37380 <![CDATA[l 3-10 ]]> 1 31290 <![CDATA[l 5-6 ]]> 1 9912 <![CDATA[l 6-12 ]]> 1 14770 <![CDATA[l 9-11 ]]> 1 11480 <![CDATA[l 12-13 ]]> 1 13244 total 11 155064

[0168] Table 7 Conventional unit planning results considering lower system expected congestion cost

[0169]

[0170] Table 8 Planning results of new energy units considering lower system expected congestion cost

[0171]

[0172] In this scenario, the clearing prices of each node in this example are shown in Table 9. It can be clearly observed from the table that the clearing prices of each node in the system tend to be balanced. The clearing prices of most nodes are 304.52 yuan / MWh. Only three nodes have clearing prices lower than this value, and the difference is small. The main reason is that these three power sources are relatively abundant, and the congestion in the system is significantly alleviated at this time.

[0173] Table 9 System node clearing electricity prices considering lower expected congestion costs

[0174] node Node electricity price (yuan / MWh) node Node electricity price (yuan / MWh) 1 304.52 8 296.78 2 304.52 9 292.26 3 304.52 10 304.52 4 304.52 11 294.73 5 304.52 12 304.52 6 304.52 13 304.52 7 304.52

[0175] In this scenario, the clearing prices of different entities in the electricity spot market in the example are shown in Table 10. It can be clearly observed from the table that the clearing prices of various entities in the electricity spot market tend to be consistent, the clearing prices of wind turbines and photovoltaic power stations are slightly lower than the load, and the clearing prices of gas turbines are slightly higher than the load, only 4.32 yuan / MWh higher, indicating that the role of the market in optimizing resource allocation has been fully played at this time, but under this planning scheme, the transmission lines have not been fully utilized, resulting in a huge waste of existing resources.

[0176] Table 10: Clearing prices of different entities in the market when considering lower expected congestion costs

[0177] Clearing electricity price (yuan / MWh) Load side 302.97 Coal-fired units 303.55 Gas-electric unit 307.29 Wind turbines 297.77 Photovoltaic power station 299.24

[0178] (3) Grid planning in the electricity spot market environment considering moderate system expected congestion costs

[0179] Based on the above analysis, we choose to set the appropriate system expected congestion cost under the power spot market environment, solve the model, and the transmission line planning results are shown in Table 11, the conventional unit planning results are shown in Table 12, and the new energy unit planning results are shown in Table 13. From the results in the table, we can see that in this scenario, the system newly builds 9 transmission lines, and the total investment cost of the transmission lines is 1270.5 million yuan, which is 2 lines less than the case of considering a lower system expected congestion cost, and the total investment cost of the transmission lines is reduced by 18.07%; the newly added conventional units total 8400MW, the newly added new energy units total 18820MW, and the total investment of various types of generators is 49.95 billion yuan. Under the power spot market environment, considering a relatively moderate system expected congestion cost, the line investment cost and the unit investment cost in the planning results are both increased compared with the planning results without considering the power spot market, and the lower system expected congestion cost is reduced. At this time, while ensuring that the power spot market fully plays the role of resource allocation, it also effectively guarantees the economy of the power system.

[0180] Table 11 Route planning results considering moderate system expected congestion cost

[0181]

[0182]

[0183] Table 12 Conventional unit planning results considering moderate system expected congestion cost

[0184]

[0185] Table 13 Planning results of new energy units considering moderate system expected congestion cost

[0186]

[0187] In this scenario, the clearing prices of each node in this example are shown in the table. It can be clearly observed from the table that the clearing prices of each node in the system tend to be balanced, and the clearing prices of most nodes are 304.52 yuan / MWh. Only four nodes have clearing prices lower than this value, and the difference is small. The main reason is that these four power sources are relatively abundant. It can be seen from the table that the congestion in the system has been significantly alleviated at this time, and the congestion cost of the system is low; and the clearing price is close to that when considering the lower expected congestion cost of the system, indicating that the power spot market has also fully played a role in resource allocation.

[0188] Table 14 Node clearing electricity prices considering moderate expected congestion costs

[0189]

[0190]

[0191] In this scenario, the clearing prices of different entities in the electricity spot market in the example are shown in Table 15. It can be clearly observed from the table that the clearing prices of various entities in the electricity spot market tend to be consistent, indicating that the role of the market in optimizing resource allocation has been fully exerted, and under this planning scheme, the resources in the system have also been fully utilized.

[0192] Table 15 Clearing prices of different entities in the market when considering moderate expected congestion costs

[0193] Clearing electricity price (yuan / MWh) Load side 303.52 Coal-fired units 304.59 Gas-electric unit 309.56 Wind turbines 295.44 Photovoltaic power station 298.51

[0194] The comparison of system planning results in different scenarios is shown in Figure 3. It can be seen from the figure that when the electricity spot market is not considered, the planning scheme has the lowest investment cost and the best economic benefit, but the utilization rate of the transmission line is high and the system is seriously blocked; when the excessive pursuit of electricity price balance between nodes in the power system will cause a significant increase in system investment costs. At the same time, the utilization rate of transmission lines is less than 85%, resulting in serious waste of resources; therefore, in the process of investment decision-making, attention should be paid to finding a balance between reducing the total investment and construction cost of the system and maintaining the balance of electricity prices within the system.

[0195] In order to give full play to the guiding role of price signals in the electricity spot market, the invention first constructs a power generation and transmission expansion planning model based on the participation of new energy in the electricity spot market environment, and the model consists of a main problem of power generation and transmission expansion planning and a sub-problem of the electricity spot market clearing algorithm; secondly, the Benders decomposition method is used to solve the model, and finally, the model is simulated and analyzed through a provincial power grid in East China. Compared with the planning scheme without considering the electricity spot market environment, the planning scheme considering the electricity spot market environment with a high proportion of new energy participation will increase the investment and construction cost of the power system to a certain extent, but the clearing electricity prices in various regions of the power system are relatively close, and the guiding role of the price signal of the electricity spot market has been fully exerted.

Claims

1. A method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market, characterized in that: The following steps are involved: S1. Establish a power generation and transmission planning model; S2. Construct the objective function and constraints of power generation and transmission planning; S3. Solve the electricity spot market sub-problem.

2. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 1 is characterized by: The power generation and transmission planning model includes an upper-level main problem model and a lower-level sub-problem model, and the upper-level main problem model is a power generation and transmission expansion planning model, and the lower-level sub-problem model is an electricity spot market clearing model. The upper-level main problem model and the lower-level sub-problem model are set with their own different optimization objectives and constraints. At the same time, the optimization objective function and constraints of the upper-level main problem model are related to the decision variables of the model at this level and the optimal solution of the optimization problem of the lower-level sub-problem model. The optimal solution of the lower-level sub-problem model is also affected by the decision variables of the upper-level main problem model.

3. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 2 is characterized by: The upper main problem model is: The lower level sub-problem model is: Where x and y are the decision variables of the upper and lower models, respectively; F and f are the optimization objectives of the upper and lower models, respectively; and H and h are the related constraints of the upper and lower models, respectively.

4. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 1, characterized in that: The objective function of power generation and transmission planning includes the objective function of the power generation and transmission expansion planning model, and the objective function of the power generation and transmission expansion planning model is to minimize the total cost of the power system, and specifically includes the power system investment cost and the power system operation cost. The calculation formulas for investment cost and operation cost are: my C all =C invest +C op Where, Ω G ,Ω L ,Ω W and Ω PV are respectively the set of conventional units, the set of transmission lines, the set of wind turbines and the set of photovoltaic generators; C all is the total investment and operating costs; C invest and C op They are the sum of the system's investment costs and operating costs respectively; and are the investment costs of traditional generator set g, transmission line l, wind turbine set w and photovoltaic power station pv respectively; and They are all Boolean variables, representing the construction status of conventional units g, transmission lines l, wind turbines w, and photovoltaic power stations pv, respectively. 0 means not in construction, and 1 means in construction. is the operating cost of the traditional unit g; and are the power abandonment penalty costs of wind turbine w and photovoltaic power station pv, and They are the output of the conventional unit g, the actual output of the wind turbine unit w and the actual output of the photovoltaic power station pv at time t respectively; and They are the predicted output of wind turbine w and photovoltaic power station pv respectively.

5. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 1 is characterized in that: The constraints of power generation and transmission planning include decision variable constraints, node power balance constraints, line flow constraints, conventional unit output constraints, new energy unit output constraints and node phase angle constraints; The decision variable constraints are: Where: and They are the set of conventional units that have been put into operation, the set of transmission lines, the set of wind farms and the set of photovoltaic power stations, which constrain the situation of the traditional units, transmission lines and new energy units that have been put into operation in the system; The node power balance constraint is: Where: is the active power transmitted by transmission line l; is the set of traditional units located at node i; Ω B is a set of nodes, s(l) and r(l) are the sending and receiving ends of line l respectively, p i,t is the load forecast value of node i at time t; The line power flow constraint is: Where: P l Lmax is the maximum active transmission power of transmission line l; is the reactance of transmission line l; δ s(l),t and δ r(l),t is the phase angle value between the sending end and the receiving end of the transmission line l; The conventional unit output constraint is: Where: is the installed capacity of conventional unit g; The output constraints of the new energy units are: Where: is the predicted output of wind turbine w at time t; is the predicted output of the photovoltaic power station pv at time t; The node phase angle constraint is: Where: is the reference node set, δ max is the maximum phase angle value of the node; δ i,t is the voltage phase angle of node i in year t.

6. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 1, characterized in that: Solving the electricity spot market sub-problem includes clearing the spot market of the upper-level power grid planning results based on time-series production simulation technology, calculating the system congestion cost, and returning it to the upper-level planning model to optimize the transmission network planning results.

7. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 6 is characterized by: To ensure that the subproblem always has a solution, an auxiliary variable s is introduced into the subproblem l,t , to characterize the line congestion in the sub-problem. The objective function of the safety constraint unit combination in the sub-problem includes the winning bid price of conventional units and new energy units, the start-up and shutdown cost of conventional units, and the system congestion cost, which is expressed as: min C G +C W +C PV +C SU +C SD +C cong Where: C G is the winning bid price of conventional units, C W is the winning bid price of the wind turbine, C PV is the winning bid price of the solar power unit, C SU is the startup cost of conventional units, C SD is the downtime cost of conventional units, is the blocking penalty cost, s l,t is an auxiliary variable, indicating the line blocking situation in the sub-problem; Since the upper-level planning main problem has obtained the new planning grid of the system, the upper and lower limit constraints of the unit output are expressed as: Where: and They are the planning results of conventional units g, wind turbines w and photovoltaic power stations pv obtained by solving the power generation and transmission expansion planning model of the node; are the power output of conventional unit g, wind turbine unit w and photovoltaic power station pv at time t respectively; is a Boolean variable, indicating the start and stop status of the conventional unit g at time t; Indicates that the unit is in the starting state. Indicates that the unit is in shutdown state; Similarly, the system power balance constraint can be modified as follows: Where: P t * is the system load value at time t, which can be obtained from the transmission expansion planning model; The transmission line power flow constraint can be modified as follows: Where: P l max* To solve the power generation and transmission expansion planning model, we need to obtain the maximum transmission capacity and Ω of the transmission line l. B is the node set, G l,i The generator output power transfer distribution factor for the node i where the generator set is located on the line l.

8. The method for planning the expansion of power generation and transmission of new energy sources participating in the power spot market according to claim 7 is characterized by: The system reserve constraint is, Where: u g,t is a Boolean variable, indicating the start and stop status of the conventional unit g at time t, u g,t =1 means the unit is in the starting state, u g,t =0 means the unit is in shutdown state; and are the minimum technical output and maximum technical output of conventional unit g respectively; and They are the upper and lower reserve requirements of the system. In the actual operation of the power system, load fluctuations and unexpected failures of power equipment often occur. The power system uses reserve constraints to deal with these randomness to ensure the safety and reliability of power supply; The unit climbing capability constraint is: Where: and are the up-climbing capacity and down-climbing capacity of the unit in one time step, respectively. M is a very large positive integer, which is used to ensure the stable operation of the power system. The output change of the conventional unit in a continuous period must comply with the limit of its climbing capacity. Minimum start and stop time constraints, Where: is the time that the conventional unit g still needs to continue to operate at time t; is the time that the conventional unit g still needs to be shut down at time t; is the minimum continuous startup time of the conventional unit g, is the minimum continuous downtime of conventional unit g; The objective function of the safety-constrained economic dispatch in the sub-problem reduces the start-up and shutdown costs of conventional units. The objective function of the safety-constrained economic dispatch in the sub-problem is: min C G +C W +C PV +C SU +C SD +C cong The constraints of the security-constrained economic dispatch in the subproblem, the system congestion price component can be the dual value generated by the transmission line flow constraint and It is expressed as: By adding the electricity spot market clearing model as a sub-problem of the two-level programming model, it is used to obtain the objective function, constraints and decision variables of the main problem of transmission and generation expansion planning and the optimal solution of the electricity spot market clearing optimization problem.

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