Source-grid-load-storage coordinated planning method and system in electricity market environment

Abstract

The invention provides a source-grid-load-storage coordinated planning method and system in an electricity market environment, and the method comprises the steps: firstly constructing a source-grid-load-storage coordinated planning model which takes the source-load uncertainty of an upper layer into consideration and takes the minimum total cost of an electric power system as a target; the lower layer is a double-layer planning model of an electric power spot market clearing model with minimum power generation cost as a target, then solving the upper-layer planning model by adopting a Hemma optimization algorithm to obtain a source-grid-load-storage coordinated planning initial scheme, and then inputting the source-grid-load-storage coordinated planning initial scheme into a lower-layer clearing model to obtain a source-grid-load-storage coordinated planning model; and a commercial solver is adopted to solve the lower-layer clearing model to obtain a clearing price, and then the clearing price is fed back to the upper-layer planning model for loop iteration to obtain a final source-network-load-storage coordinated planning scheme. According to the invention, the combination of the source-network-load-storage coordinated planning and the power market clearing is realized, the consumption rate of new energy and the operation safety, economy and stability of a novel power system are improved, and the solving efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical engineering, and particularly relates to a coordinated planning method and system for power sources, grids, loads, and energy storage under the power market environment. Background Art

[0002] With the increase in the penetration rate of new energy, a new power system dominated by a high proportion of new energy is taking shape. Due to the uncertainties of new energy such as wind power and photovoltaic power, and the deviation of load growth prediction, the power generation and transmission planning of the power system has faced new challenges. Under this background, the "power source follows the load" mode will no longer be applicable. Therefore, from the perspective of "power source-grid-load-energy storage" coordination, corresponding scenarios and constraint conditions need to be introduced into the planning model to construct a "power source-grid-load-energy storage" coordinated planning model to adapt to the new power system form. At the same time, in the power spot market environment, the wide access of new energy not only affects the operation of the spot market, but also increases the uncertainty and complexity of system operation, thus posing higher requirements for the coordination of the four aspects of "power source-grid-load-energy storage". A "power source-grid-load-energy storage" coordinated planning model is proposed as Figure 1 shown.

[0003] From Figure 1 it can be seen that the model includes four types of entities, namely: the power source side, the grid side, the load side, and the energy storage side. Among them, the power source side includes thermal power units, gas turbine units, hydropower units, and new energy units; the grid side includes the grid network structure and tie lines; the load side considers incentive-based demand response; and the energy storage side includes energy storage devices such as electrochemical energy storage and large-capacity energy storage.

[0004] In recent years, many scholars have conducted in-depth research on the coordinated planning of "power source-grid-load-energy storage". Some have studied the impact of energy storage configuration on transmission grid planning and proposed an integrated planning method of "power source-energy storage-grid" based on this. Some have comprehensively considered various policy factors and established a new power system generation planning model considering the coordinated and optimized operation of power sources, grids, loads, and energy storage. When establishing the "power source-grid-load-energy storage" coordinated planning model, the above-mentioned literatures have all weakened the impact of power market rules on the planning results. Some studies have deeply explored the integration of power sources, grids, and loads under the premise of liberalizing the power sales side and incremental distribution grids to coordinate the reasonable and orderly development of distribution grids and ensure the sustainability and reliability of power supply under the background of power market reform. Some have comprehensively considered the interests of multiple entities in the power market and marketization factors and proposed a planning model for the coordinated interaction of multiple entities of power sources, grids, and loads based on game theory. Although the above-mentioned literatures have considered the impact of the power spot market on power system planning to a certain extent, most of the studies are deterministic planning and do not comprehensively consider the impact of the uncertainties of power sources and loads on the planning results. Summary of the Invention

[0005] The objective of the present invention is to provide a coordinated planning method and system for source-grid-load-storage in the electricity market environment in view of the above problems existing in the prior art.

[0006] To achieve the above objective, the technical solution of the present invention is as follows:

[0007] In the first aspect, the present invention proposes a coordinated planning method for source-grid-load-storage in the electricity market environment, including:

[0008] S1. Construct a bi-level programming model, where the upper layer is a coordinated planning model for source-grid-load-storage considering the uncertainty of source-load and aiming at minimizing the total cost of the power system, and the lower layer is a clearing model for the electricity spot market aiming at minimizing the generation cost;

[0009] S2. Use the hippopotamus optimization algorithm to solve the upper-layer planning model to obtain an initial coordinated planning scheme for source-grid-load-storage;

[0010] S3. Input the initial coordinated planning scheme for source-grid-load-storage into the lower-layer clearing model, and use a commercial solver to solve the lower-layer clearing model to obtain the clearing price;

[0011] S4. Feed back the clearing price to the upper-layer planning model for iterative calculation to obtain the final coordinated planning scheme for source-grid-load-storage.

[0012] The said S2 includes:

[0013] S21. Initialize the hippopotamus population, calculate the fitness function value of each hippopotamus individual in the population, and determine the optimal position and the optimal fitness value. Among them, the fitness function is the objective function of the coordinated planning model for source-grid-load-storage;

[0014] S22. Calculate the sensitivity of each control variable respectively, and determine the sensitivity ratio of each control variable, so as to obtain the iterative speed factor of each control variable, and then adjust its initial iterative speed according to the iterative speed factor of each control variable;

[0015] S23. Update the individual position in turn according to the position update strategies of the hippopotamus exploration stage, the predator defense stage, and the predator escape stage;

[0016] S24. Determine whether the iterative termination condition is satisfied. If it is satisfied, output the optimal planning scheme; if not, return to S23 for the next iteration.

[0017] The sensitivity of each control variable is calculated according to the following formula:

[0018]

[0019] In the above formula, Δx i , ΔC allare the i-th control variable and the change in the total cost of the power system, respectively, is the sensitivity of the i-th control variable;

[0020] The proportion of the sensitivity of each control variable is calculated according to the following formula:

[0021]

[0022] In the above formula, is the proportion of the sensitivity of the i-th control variable, and I is the set of control variables;

[0023] The adjusted initial iteration speed of each control variable is calculated according to the following formula:

[0024]

[0025] In the above formula, is the adjusted initial iteration speed of the i-th control variable, and v0 is the initial iteration speed.

[0026] The objective function of the source-network-load-storage coordinated planning model includes:

[0027] minC all = C invest + C op + C DR

[0028]

[0029] In the above formula, C all is the total cost of the power system, and C invest , C op , C DR are the investment cost, operation cost, and compensation cost of the interruptible load respectively, are the investment costs of the soc of conventional units, transmission lines, wind farms, photovoltaic power plants, and energy storage power plants respectively, are the investment decision variables of conventional units g, transmission lines l, wind farms w, photovoltaic power plants pv, and energy storage power plants soc respectively, Ω G , Ω L , Ω W , Ω PV , Ω SOC are the sets of conventional units, transmission lines, wind farms, photovoltaic power plants, and energy storage power plants respectively, and Ω T , Ω B are the sets of time and interruptible load respectively, are the operation costs of conventional units g, wind farms w, photovoltaic power plants pv, and energy storage power plants soc respectively, is the output of conventional unit g at time t, The output of wind farm w and its maximum value at time t are respectively, The output of PV power station pv and its maximum value at time t are respectively, The discharge power of energy storage station soc and the load shedding amount of interruptible load i at time t are respectively, The compensation cost per unit of electricity for interruptible load i;

[0030] The constraint conditions include decision variable constraints, node power balance constraints, interruptible load user load shedding constraints, energy storage station capacity constraints, energy storage station charge and discharge constraints, the relationship constraints between the state of charge of the energy storage and the charge and discharge power, line power flow constraints, conventional unit output constraints, new energy unit output constraints, and node phase angle constraints. Among them, the node power balance constraints are described in the form of chance constraints.

[0031] The electricity spot market clearing model includes a security-constrained unit commitment sub-model, a security-constrained economic dispatch sub-model, and an electricity price calculation sub-model;

[0032] The objective function of the security-constrained unit commitment sub-model includes:

[0033]

[0034] In the above formula, c g,k , P g,k,t Are respectively the bid price and the winning bid electricity quantity of conventional unit g in section k, Are respectively the start-up and shutdown costs of conventional unit g at time t, c w,r , c pv,r Are respectively the bid prices of wind turbine w and PV power station pv in section r, P w,r,t , P pv,r,t Are respectively the winning bid electricity quantities of wind turbine w and PV power station pv in section r, Ω G , Ω L , Ω T , Ω W , Ω PV Are respectively the sets of conventional units, transmission lines, time, wind farms, and PV power stations, Ω Nk , Ω Nr Are respectively the sets of the number of bid price segments of conventional units and new energy units, C cong Is the system congestion cost, Is the congestion penalty cost of transmission line l, s l,t Is the congestion variable of transmission line l at time t;

[0035] The constraint conditions include system reserve constraints, unit output upper and lower limit constraints, unit ramping ability constraints, minimum start-stop time constraints, system power balance constraints, and transmission line power flow constraints;

[0036] The objective function of the security-constrained economic dispatch sub-model includes:

[0037]

[0038] The constraint conditions include system reserve constraints, upper and lower limits of generator output constraints, system power balance constraints, and transmission line power flow constraints;

[0039] The electricity price calculation sub-model is:

[0040]

[0041] In the above formula, y i is the clearing electricity price of the i-th node, is the electricity energy price component of the i-th node, is the congestion price component of the i-th node;

[0042] The above-mentioned S3 includes: first solving the security-constrained unit commitment sub-model to obtain the start-stop states of each generating unit, then solving the security-constrained economic dispatch sub-model according to the start-stop states of each generating unit to obtain the output of each generating unit, and finally calculating the clearing electricity price through the electricity price calculation sub-model.

[0043] In a second aspect, the present invention proposes a source-network-load-storage coordinated planning system in a power market environment, including a model construction module, an upper-layer model solving module, and a lower-layer model solving module;

[0044] The model construction module is used to construct a two-layer planning model. The upper layer of this model is a source-network-load-storage coordinated planning model considering source-load uncertainty and aiming at minimizing the total cost of the power system, and the lower layer is a power spot market clearing model aiming at minimizing the generation cost;

[0045] The upper-layer model solving module is used for:

[0046] Using the hippopotamus optimization algorithm to solve the upper-layer planning model iteratively to obtain an initial solution for source-network-load-storage coordinated planning;

[0047] Iterating according to the clearing price fed back by the lower-layer model to obtain a final solution for source-network-load-storage coordinated planning;

[0048] The lower-layer model solving module is used to input the initial solution for source-network-load-storage coordinated planning into the lower-layer clearing model and use a commercial solver to solve the lower-layer clearing model, and feed back the obtained clearing price to the upper-layer planning model for iterative calculation.

[0049] The solution process of the upper-layer model solving module includes:

[0050] A1. Initialize the hippopotamus population, calculate the fitness function values of each hippopotamus individual in the population, and determine the optimal position and the optimal fitness value, where the fitness function is the objective function of the source-network-load-storage coordinated planning model;

[0051] A2. Calculate the sensitivities of each control variable respectively, and determine the sensitivity ratio of each control variable, so as to obtain the iterative speed factor of each control variable, and then adjust its initial iterative speed according to the iterative speed factor of each control variable;

[0052] A3. Update the individual positions in turn according to the position update strategies of the hippopotamus exploration stage, predator defense stage, and escaping predator stage;

[0053] A4. Judge whether the iterative termination condition is satisfied. If it is satisfied, output the optimal planning scheme; if not, return to A3 for the next iteration.

[0054] The sensitivities of the control variables are calculated according to the following formula:

[0055]

[0056] In the above formula, Δx i , ΔC all are the change amounts of the i-th control variable and the total power system cost respectively, is the sensitivity of the i-th control variable;

[0057] The sensitivity ratio of each control variable is calculated according to the following formula:

[0058]

[0059] In the above formula, is the sensitivity ratio of the i-th control variable, and I is the set of control variables;

[0060] The adjusted initial iterative speed of each control variable is calculated according to the following formula:

[0061]

[0062] In the above formula, is the adjusted initial iterative speed of the i-th control variable, and v0 is the initial iterative speed.

[0063] The objective function of the source-network-load-storage coordinated planning model includes:

[0064] minC all = C invest + C op + C DR

[0065]

[0066] In the above formula, C all is the total cost of the power system, C invest , C op , C DR are the investment cost, operation cost, and compensation cost of the interruptible load respectively, are the investment costs of the soc of conventional units, transmission lines, wind farms, photovoltaic power plants, and energy storage power plants respectively, are the investment decision variables of conventional unit g, transmission line l, wind farm w, photovoltaic power plant pv, and energy storage power plant soc respectively. Ω G , Ω L , Ω W , Ω PV , Ω SOC are the sets of conventional units, transmission lines, wind farms, photovoltaic power plants, and energy storage power plants respectively. Ω T , Ω B are the sets of time and interruptible load respectively, are the operation costs of conventional unit g, wind farm w, photovoltaic power plant pv, and energy storage power plant soc respectively, is the output of conventional unit g at time t, are the output of wind farm w and its maximum value at time t respectively, are the output of photovoltaic power plant pv and its maximum at time t respectively, are the discharge power of energy storage power plant soc and the load shedding amount of interruptible load i at time t respectively, is the unit power compensation cost of interruptible load i;

[0067] The constraint conditions include decision variable constraints, node power balance constraints, interruptible load user load shedding constraints, energy storage power plant capacity constraints, energy storage power plant charge and discharge constraints, relationship constraints between energy storage state of charge and charge and discharge power, line power flow constraints, conventional unit output constraints, new energy unit output constraints, and node phase angle constraints. Among them, the node power balance constraints are described in the form of chance constraints.

[0068] The power spot market clearing model includes a security-constrained unit commitment sub-model, a security-constrained economic dispatch sub-model, and a power price calculation sub-model;

[0069] The objective function of the security-constrained unit commitment sub-model includes:

[0070]

[0071] In the above formula, c g,k , P g,k,t are the bid price and the winning bid electricity quantity of conventional unit g in section k respectively, are the start-up and shutdown costs of the conventional unit g at time t, c w,r , c pv,r are the bids of the wind turbine w and the PV power station pv in the segment r, P w,r,t , P pv,r,t are the winning bid electricity quantities of the wind turbine w and the PV power station pv in the segment r, Ω G , Ω L , Ω T , Ω W , Ω PV are the sets of conventional units, transmission lines, time, wind farms, and PV power stations, Ω Nk , Ω Nr are the sets of the number of bid segments of conventional units and new energy units, C cong is the system congestion cost, is the congestion penalty cost of the transmission line l, s l,t is the congestion variable of the transmission line l at time t;

[0072] The constraint conditions include system reserve constraints, upper and lower limits of unit output constraints, unit ramp rate constraints, minimum start-up and shutdown time constraints, system power balance constraints, and transmission line power flow constraints;

[0073] The objective function of the security-constrained economic dispatch sub-model includes:

[0074]

[0075] The constraint conditions include system reserve constraints, upper and lower limits of unit output constraints, system power balance constraints, and transmission line power flow constraints;

[0076] The electricity price calculation sub-model is:

[0077]

[0078] In the above formula, y i is the clearing electricity price of the i-th node, is the electricity energy price component of the i-th node, is the congestion price component of the i-th node;

[0079] The solution process of the lower-layer model solving module includes: first solving the security-constrained unit commitment sub-model to obtain the start-stop states of each generating unit, then solving the security-constrained economic dispatch sub-model according to the start-stop states of each generating unit to obtain the output of each generating unit, and finally calculating the clearing electricity price through the electricity price calculation sub-model.

[0080] Compared with the prior art, the beneficial effects of the present invention are:

[0081] 1. A method for coordinated planning of source-grid-load-storage in a power market environment first constructs a two-layer programming model with an upper layer being a coordinated planning model of source-grid-load-storage that considers the uncertainty of source-load and aims to minimize the total cost of the power system, and a lower layer being a clearing model of the power spot market that aims to minimize the generation cost. Then, the hippopotamus optimization algorithm is used to solve the upper-layer planning model to obtain an initial solution for the coordinated planning of source-grid-load-storage. Next, the initial solution for the coordinated planning of source-grid-load-storage is input into the lower-layer clearing model, and a commercial solver is used to solve the lower-layer clearing model to obtain the clearing price. Subsequently, the clearing price is fed back to the upper-layer planning model for iterative calculation to obtain the final solution for the coordinated planning of source-grid-load-storage. On the one hand, this method combines the coordinated planning of source-grid-load-storage with the clearing of the power market. By configuring energy storage and encouraging interruptible loads to participate in scheduling, it can effectively reduce the total cost of system construction and operation, improve the ability to accommodate new energy, and further enhance the economy and cleanliness of the power system. On the other hand, for the two-layer programming problem, this method uses the hippopotamus optimization algorithm and a commercial solver for collaborative solution, effectively improving the solution efficiency.

[0082] 2. A method for coordinated planning of source-grid-load-storage in a power market environment, according to the characteristics of the power system, after initializing the hippopotamus population, calculates the sensitivity and sensitivity ratio of each control variable respectively to obtain the iterative speed factor of each control variable, and then adjusts its initial iterative speed according to the iterative speed factor of each control variable. This strategy of adjusting the iterative speed based on sensitivity can further improve the solution efficiency of the algorithm.

[0083] 3. A method for coordinated planning of source-grid-load-storage in a power market environment adjusts the adaptability of the planning scheme to the stochastic uncertainty of source-load through chance constraints, realizing the effective handling of two uncertainty factors. Description of the Drawings

[0084] Figure 1 is the coordinated planning model of "source-grid-load-storage".

[0085] Figure 2 is the network topology of a provincial power grid in the East China region in Embodiment 1.

[0086] Figure 3 is the clustering result of load and new energy output in Embodiment 1.

[0087] Figure 4 is the typical daily curve of wind power, photovoltaic power and load in Embodiment 1.

[0088] Figure 5 is the framework diagram of the method described in Embodiment 1.

[0089] Figure 6 is the clearing framework of the power spot market.

[0090] Figure 7 This is the comparison of the planning results.

[0091] Figure 8 This is the structure diagram of the system described in Embodiment 2. Specific implementation manners

[0092] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0093] Embodiment 1:

[0094] In this embodiment, a provincial power grid of 500 kV and above in East China (the specific power grid topology is as Figure 2 shown. At nodes 3, 4, 10, and 11 of the system, the upper limits of the installed capacity of wind power are 3200 MW, 6400 MW, 11200 MW, and 11200 MW respectively. At nodes 1, 3, 4, 10, and 13 of the system, the upper limits of the installed capacity of photovoltaic power are 9000 MW, 7200 MW, 7200 MW, 6300 MW, and 6300 MW respectively. The maximum load of the system is 130722.2 MW, and the installed capacity of conventional units is 104811.0 MW. In this section, it is assumed that the investment cost of all newly built 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; the investment cost of gas-fired units is 0.8 million yuan / MW. The load and the output curves of wind power and photovoltaic power used are all analyzed based on the historical data of the wind and light output and the load in the region throughout the year of 8760 h, and the obtained clustering results are as Figure 3 shown. This method fully considers the temporal and correlation characteristics of the wind and light output and the load in the region, and at the same time shows the variation characteristics of the wind and light output within a day. Subsequently, by calculating the Pearson correlation coefficient, the class with the highest correlation with the overall data is selected as the typical daily curves of wind power output, photovoltaic power output, and load, as Figure 4 shown, and based on this, the planning model is solved) is taken as the research object, and a method for coordinated planning of power sources, grids, loads, and energy storage under a power market environment described in the present invention is implemented, as Figure 5 shown. The specific steps are as follows:

[0095] 1. Establish a coordinated planning model for power sources, grids, loads, and energy storage

[0096] Objective function

[0097] The objective function of the power transmission and expansion planning model is usually to minimize the total cost of the power system, which specifically includes the following two parts: the investment cost of the power system and the operating cost of the power system. During the rapid growth of new energy, due to the mismatch between the transmission system planning and the flexible resource allocation, a relatively serious power curtailment situation has occurred. Therefore, in the power system planning, the coordinated planning of "source-grid-load-storage" should be considered to ensure the flexibility and reliability of the system. Further considering factors such as the investment and operation costs of the energy storage system and the compensation cost of the curtailable load, the following objective function is constructed:

[0098] minC all =C invest +C op +C DR (1)

[0099]

[0100] In the above formula, C all is the total cost of the power system, C invest , C op , C DR are the investment cost, operating cost and compensation cost of the interruptible load respectively, are the investment costs of the state of charge (SOC) of conventional units, transmission lines, wind farms, photovoltaic power stations and energy storage power stations respectively, are the investment decision variables of conventional units g, transmission lines l, wind farms w, photovoltaic power stations pv and energy storage power stations SOC respectively, Ω G , Ω L , Ω W , Ω PV , Ω SOC are the sets of conventional units, transmission lines, wind farms, photovoltaic power stations and energy storage power stations respectively, Ω T , Ω B are the sets of time and interruptible load respectively, are the operating costs of conventional units g, wind farms w, photovoltaic power stations pv and energy storage power stations SOC respectively, is the output of conventional unit g at time t, are the output of wind farm w at time t and its maximum value respectively, are the output of photovoltaic power station pv at time t and its maximum respectively, are the discharge power of energy storage power station SOC and the load shedding amount of interruptible load i at time t respectively, is the unit power compensation cost of interruptible load i.

[0101] Constraint conditions

[0102] 1) Decision variable constraints:

[0103] In addition to the decision variable constraints of conventional units, transmission lines, and new energy units, the decision variable constraints of energy storage power stations should also be included.

[0104]

[0105] In the above formula, represent the sets of in-service conventional units, transmission lines, wind farms, and photovoltaic power stations respectively, is the set of in-service energy storage power stations.

[0106] 2) Node power balance constraint:

[0107] To address the uncertainty of source-load, an opportunity-constrained programming model is selected to describe it, specifically as follows:

[0108]

[0109] In the above formula, is the active power transmitted by transmission line l at time t, P i,t is the load value of node i at time t, α is the set confidence level, r(l) and s(l) are the receiving end and sending end of transmission line l respectively, represent the sets of traditional units, wind farms, and photovoltaic power stations located at node i respectively.

[0110] After considering the energy storage power station and incentive-based demand-side response, the above formula can be modified to the following opportunity constraint formula:

[0111]

[0112] In the above formula, is the charging power of the energy storage power station's soc at time t, P i,t ′ is the local load after the interruptible load i cuts the load response at time t, is the set of energy storage power stations located at node i.

[0113] Based on the relevant theoretical basis of probability theory, the above opportunity constraint formula can be transformed into the following deterministic constraint formula:

[0114]

[0115] In the above formula, F -1 is the inverse function.

[0116] 3) Interruptible load user load shedding constraint:

[0117]

[0118] In the above formula, P i DRmaxis the maximum load curtailment amount for the interruptible load i.

[0119] 4) Energy storage power station capacity constraint:

[0120]

[0121] In the above formula, is the charge amount of the energy storage power station's state of charge (SOC) at time t, and are the minimum and maximum charge amounts of the energy storage power station's SOC.

[0122] 5) Energy storage power station charge and discharge constraint:

[0123]

[0124] In the above formula, are Boolean variables, representing the charging state and discharging state of the energy storage power station's SOC at time t, respectively. 0 indicates no charge / discharge, and 1 indicates charge / discharge.

[0125] In equations (16) and (17), there are cases of multiplying two Boolean variables, which are non - linear constraints. Therefore, the big M method is used to linearize this constraint. Equation (16) can be modified as shown in equations (19) and (20), and equation (17) can be modified as shown in equations (21) and (22):

[0126]

[0127]

[0128] In the above formula, M is a maximum constant.

[0129] 6) Relationship constraint between the state of charge of energy storage and charge - discharge power:

[0130]

[0131] In the formula: η ch 、η dis are the charging efficiency and discharging efficiency of the energy storage power station's SOC, respectively.

[0132] 7) Other constraints, including:

[0133] Line power flow constraint

[0134]

[0135] In the above formula, P l Lmax is the maximum active power transmission capacity of the transmission line l, is the reactance of the transmission line l, δ s(l),t 、δr(l),t They are the phase angle values at the sending end and receiving end of the transmission line l, respectively.

[0136] Conventional unit output constraint

[0137]

[0138] In the above formula, is the installed capacity of the conventional unit g.

[0139] New energy unit output constraint

[0140]

[0141] In the above formula, are the investment decision variables of the wind turbine w and the PV power station pv at time t, respectively;

[0142] Node phase angle constraint:

[0143] -δ max ≤δ i,t ≤δ max , i ∈ Ω B , t ∈ Ω T (29)

[0144]

[0145] In the above formula, is the set of reference nodes, δ max is the maximum phase angle value of the node; δ i,t is the voltage phase angle of node i at time t.

[0146] 2. Establish the clearing model of the electricity spot market.

[0147] The clearing model of the electricity spot market includes three parts: the security-constrained unit commitment sub-model, the security-constrained economic dispatch sub-model, and the electricity price calculation sub-model. Its clearing framework is as Figure 4 shown. By solving them in sequence, the clearing results of the electricity spot market can be obtained, including: the winning electricity quantity of each unit in each time period, the on / off state, the clearing electricity price, the system carbon emission situation, etc.

[0148] The security-constrained unit commitment sub-model is used to optimize the operation plan of the units on the power supply side and determine the start / stop state of the units to maximize the economic efficiency while meeting the security constraints of the power system. To ensure that this sub-problem always has a solution, the auxiliary variable s l,t is introduced to characterize the line congestion situation in the sub-problem. The objective function of the security-constrained unit commitment sub-model includes the winning electricity prices of conventional units and new energy units, the start / stop costs of conventional units, and the system congestion costs, and can be expressed as:

[0149]

[0150] In the above formula, c g,k , P g,k,t are respectively the bid price and the winning bid electricity quantity of the conventional unit g in segment k, are respectively the start-up and shutdown costs of the conventional unit g at time t, c w,r , c pv,r are respectively the bid prices of the wind turbine w and the photovoltaic power station pv in segment r, P w,r,t , P pv,r,t are respectively the winning bid electricity quantities of the wind turbine w and the photovoltaic power station pv in segment r, Ω G , Ω L , Ω T , Ω W , Ω PV are respectively the sets of conventional units, transmission lines, time, wind farms, and photovoltaic power stations, Ω Nk , Ω Nr are respectively the sets of the number of bid price segments of conventional units and new energy units, C cong is the system congestion cost, is the congestion penalty cost of the transmission line l, s l,t is the congestion variable of the transmission line l at time t. Usually, it is considered that the start-up and shutdown costs of the wind turbine w and the photovoltaic power station pv are 0.

[0151] The constraint conditions include:

[0152] 1) System reserve constraint

[0153] In the actual operation process of the power system, there are often unforeseen situations such as load fluctuations and accidental failures of power equipment. The power system uses reserve constraints to cope with these randomnesses, thereby ensuring the safety and reliability of power supply.

[0154]

[0155] In the above formula, u g,t is a Boolean variable representing the start-stop state of the conventional unit g at time t (u g,t =1 indicates that the unit is in the start-up state, u g,t =1 indicates that the unit is in the shutdown state), are respectively the maximum output and the minimum output of the conventional unit g, are respectively the upper reserve and the lower reserve requirements of the system.

[0156] 2) Unit output upper and lower limit constraints

[0157]

[0158] In the above formula, It is determined by the planning results obtained from the upper-level planning model.

[0159] 3) Ramp rate constraint of the unit

[0160]

[0161] In the above formula, They are the upward and downward ramp rates of the unit in a time step respectively, and M is a very large positive integer.

[0162] 4) Minimum start-stop time constraint

[0163]

[0164] In the above formula, u g,τ is the start-stop state of the conventional unit g at time τ, is the time that the conventional unit g needs to continue running at time t, is the time that the conventional unit g needs to continue shutting down at time t, is the minimum continuous on-time of the conventional unit g, is the minimum continuous off-time of the conventional unit g.

[0165] 5) System power balance constraint

[0166]

[0167] In the above formula, They are the winning bid electricity quantities of the conventional unit g, wind turbine w and PV power station pv at time t respectively, which are equal to the sum of the winning bid electricity quantities of all corresponding segments. P t is the system load value at time t, which is determined by the planning results obtained from the upper-level planning model.

[0168] 6) Transmission line power flow constraint

[0169]

[0170] In the above formula, G l,i is the power transfer factor of node i to transmission line l, is the injection power of node i at time t, P l max is the maximum transmission capacity of transmission line l, which is determined by the planning results obtained from the upper-level planning model.

[0171] 7) Energy storage constraint

[0172]

[0173] In the above formula, They are respectively the maximum charge of the energy storage power station, the minimum charging power, the maximum charging power, the minimum discharging power, and the maximum discharging power, which are determined by the planning results obtained from the upper-layer planning model.

[0174] The security-constrained economic dispatch sub-model is used to optimize the power generation of each unit by minimizing the total system power purchase cost on the premise of knowing the start-stop states of each generator set. Its objective function is:

[0175]

[0176] After the operation plan of the generator set is determined by the security-constrained unit commitment sub-model, the constraint conditions in the security-constrained economic dispatch sub-model do not need to consider the unit ramp rate constraint and the minimum start-stop time constraint, and the remaining constraints are the same as those in the security-constrained unit commitment sub-model.

[0177] The electricity price calculation sub-model uses the Locational Marginal Pricing (LMP) as the pricing model for the spot market. LMP is the marginal cost required to increase one unit of electricity at this node, reflecting the short-term power supply cost of each node in the system. Without considering transmission losses, LMP usually includes two parts, namely the system electricity energy price component and the congestion price component. Among them, the system electricity energy price component is the electricity price formed when the power system is optimized and dispatched without considering the transmission capacity limit conditions, and this component is the same at any time period and any node in the electricity spot market; while the congestion price component is generated by the system network congestion, reflecting the impact of different node loads on the network congestion, and representing the short-term marginal cost of the system using the transmission capacity. Therefore, when there is congestion in the power system, the LMP of different nodes may be different, and market participants settle according to the LMP. Through the price signal and location signal provided by the LMP, market participants can be prompted to adjust their own power generation or consumption behaviors according to market demand and costs. According to the marginal price theory, at any time t, the clearing electricity price y of any node i in the system i can be calculated by the following formula:

[0178]

[0179] In the above formula, is the electricity energy price component of the i-th node, is the congestion price component of the i-th node. Summing up the congestion price components of each node can obtain the system congestion cost, which can be represented by the dual values and generated by the transmission line power flow constraint, as follows:

[0180]

[0181] 3. Use the hippopotamus optimization algorithm to solve the upper-layer planning model and obtain the initial solution of the source-network-load-storage coordinated planning, which specifically includes:

[0182] S21. Initialize the hippopotamus population, calculate the fitness function value of each hippopotamus individual in the population, and determine the optimal position and the optimal fitness value. Among them, the hippopotamus is the candidate solution of the source-network-load-storage coordinated planning model, that is, the value of the control variable, and the fitness function is the objective function of the source-network-load-storage coordinated planning model.

[0183] S22. Calculate the sensitivities of each control variable respectively, determine the sensitivity ratio of each control variable, obtain the iterative speed factor of each control variable, and then adjust its initial iterative speed according to the iterative speed factor of each control variable. Among them, the sensitivities of each control variable are calculated according to the following formula:

[0184]

[0185] In the above formula, Δx i , ΔC all are the change amounts of the i-th control variable and the total power system cost respectively, is the sensitivity of the i-th control variable;

[0186] The control variables include the active power transmitted by the transmission line, the winning bid electricity of the conventional unit, the winning bid electricity of the wind turbine, the winning bid electricity of the photovoltaic power station, and the charge and discharge power of the energy storage power station. Therefore, the sensitivity formula can be expressed as:

[0187] ΔP l L = S xl ·ΔC all , l ∈ Ω L |r(l)=i, l ∈ Ω L |s(l)=i

[0188]

[0189] The sensitivity ratio of each control variable is calculated according to the following formula:

[0190]

[0191] In the above formula, is the sensitivity ratio of the i-th control variable, and I is the set of control variables.

[0192] Adjust the initial iterative speed of the hippopotamus optimization through the sensitivity ratio. The adjusted initial iterative speed of each control variable is calculated according to the following formula:

[0193]

[0194] In the above formula, is the adjusted initial iteration speed of the i-th control variable, and v0 is the initial iteration speed.

[0195] S23. Update the individual position according to the position update strategies of the hippopotamus exploration stage, predator defense stage, and escaping predator stage in sequence;

[0196] S24. Determine whether the iteration termination condition is satisfied. If it is satisfied, output the optimal planning scheme; if not, return to S23 for the next iteration.

[0197] 4. Input the initial scheme of the source-network-load-storage coordinated planning into the lower-level clearing model, and use the commercial solver Cplex to solve the lower-level clearing model to obtain the clearing price.

[0198] 5. Feed back the clearing price to the upper-level planning model for iterative loop to obtain the final scheme of the source-network-load-storage coordinated planning. Among them, the transmission line planning results are shown in Table 1, the conventional unit planning results are shown in Table 2, the new energy unit planning results are shown in Table 3, and the energy storage configuration is shown in Table 4:

[0199] Table 1 Line planning results in the "source-network-load-storage" coordinated planning

[0200] New line planning result Number of new lines (pieces) Cost of new lines (10,000 yuan) <![CDATA[l 2-5 > 3 31038 <![CDATA[l 2-7 > 1 24920 <![CDATA[l 1-3 > 1 5950 <![CDATA[l 9-11 > 1 11480 <![CDATA[l 12-13 > 1 13244 <![CDATA[l 5-6 > 2 19824 Total 9 106456

[0201] Table 2 Conventional unit planning results in the "source-network-load-storage" coordinated planning

[0202] Conventional unit planning result Capacity of newly built conventional units (MW) Cost of newly built conventional units (100 million yuan) 2 2500 37.5 3 1200 9.6 5 2500 37.5 Total 6200 84.6

[0203] Table 3 New energy unit planning results in the "source-network-load-storage" coordinated planning

[0204] New energy unit planning result Capacity of newly built new energy units (MW) Cost of newly built new energy units (100 million yuan) 1 3210 64.2 3 2720 57 4 4936 106.72 10 3580 84.5 13 2914 58.28 Total 17360 370.7

[0205] Table 4 Energy storage configuration results in the "source-network-load-storage" coordinated planning

[0206]

[0207] From the results in Table 1-4, it can be seen that in this scenario, the system newly builds 9 transmission lines, and the total investment cost of the transmission lines is 1064.56 million yuan. Compared with the case where the coordinated planning of the power source, grid, load, and energy storage is not considered, the total investment cost of the transmission lines is reduced by 205.94 million yuan; a total of 6200 MW of conventional generating units are newly added, and a total of 17360 MW of new energy generating units are newly added. The total investment of various generating units is 45.53 billion yuan. Compared with the case where the coordinated planning of the power source, grid, load, and energy storage is not considered, the total investment cost of various generating units is reduced by 4.42 billion yuan; 5510 MW of energy storage is newly added, and the total investment cost is 2.755 billion yuan. When comprehensively considering the coordinated planning of "power source - grid - load - energy storage", although the investment cost of energy storage construction in the system increases, the investment costs of the system's transmission lines and various generating units are both significantly reduced. Therefore, considering the coordinated planning of "power source - grid - load - energy storage" can well reduce the total cost of the construction and operation of the power system, and further improve the economy and cleanliness of the power system.

[0208] In this embodiment, the clearing electricity prices of each node are shown in Table 5:

[0209] Table 5 Clearing Electricity Prices of System Nodes in the Coordinated Planning of "Power Source - Grid - Load - Energy Storage"

[0210] Node Node electricity price (yuan / MWh) Node Node electricity price (yuan / MWh) 1 304.52 8 304.52 2 304.52 9 297.58 3 304.52 10 304.52 4 304.52 11 299.16 5 304.52 12 304.52 6 304.52 13 304.52 7 300.52

[0211] From Table 5, it can be seen that the clearing electricity prices of each node in the system tend to be balanced. The clearing electricity prices of most nodes are 304.52 yuan / MWh. Only the clearing electricity prices of three nodes are lower than this value, and the difference is small. The main reason is that there is relatively abundant power supply at these three nodes. It can be seen from the table that the blocking situation in the system has been significantly alleviated at this time, and the blocking cost of the system is low; and the clearing electricity price is close to that when only considering the power source - grid planning, indicating that through the coordinated planning method of "power source - grid - load - energy storage", while ensuring the economy and cleanliness of the power system, the resource allocation role of the power market is also played.

[0212] In this embodiment, the clearing electricity price situations of different entities in the electricity spot market are shown in Table 6:

[0213] Table 6 Clearing Electricity Price Situations of Different Entities in the Market in the Coordinated Planning of "Power Source - Grid - Load - Energy Storage"

[0214] Clearing electricity price (yuan / MWh) Load side 303.31 Coal-fired unit 303.57 Gas turbine unit 308.66 Wind turbine unit 291.73 Photovoltaic power station 296.58

[0215] As can be seen from Table 6, the clearing electricity prices of various entities in the electricity spot market tend to be the same, indicating that the role of the electricity market in optimizing resource allocation has been fully exerted, and the resources in the power system have also been fully utilized. Among them, the clearing electricity price of wind turbines is the lowest, only 277.76 yuan / MWh. Compared with wind turbines, more photovoltaic power stations are built in areas close to the load center. Therefore, its clearing electricity price is slightly higher than that of wind turbines.

[0216] Compare the results of considering the coordinated planning of "source-grid-load-storage" in this embodiment with the results obtained from the conventional power transmission and expansion planning. The results are as Figure 6 shown. From Figure 6 it can be seen that considering the coordinated planning of "source-grid-load-storage" not only reduces the investment cost of units and transmission lines in the system, but also reduces the curtailment penalty cost of wind and light by 173.47 million yuan. The reason is that when comprehensively considering the coordinated planning of "source-grid-load-storage", the system promotes the consumption of renewable energy through the configuration of energy storage and the encouragement of interruptible loads to participate in scheduling. In the case of a low source-load matching degree, the scheduling of energy storage and interruptible load users promotes the consumption of renewable energy, thereby reducing the curtailment penalty cost of wind and light and making the load curve in the system closer to the unit output curve.

[0217] Embodiment 2:

[0218] A source-grid-load-storage coordinated planning system under the electricity market environment, as Figure 7 shown, includes a model construction module, an upper-layer model solving module, and a lower-layer model solving module.

[0219] The model construction module is used to construct a two-layer planning model. The upper layer of this model is a source-grid-load-storage coordinated planning model that considers source-load uncertainty and aims to minimize the total cost of the power system, and the lower layer is an electricity spot market clearing model that aims to minimize the generation cost.

[0220] The objective function of the source-grid-load-storage coordinated planning model includes:

[0221] minC all =C invest +C op +C DR

[0222]

[0223] In the above formula, C all is the total cost of the power system, and C invest , C op , C DR are the investment cost, operation cost, and compensation cost of interruptible loads respectively, They are the investment costs of the SOC of conventional units, transmission lines, wind farms, photovoltaic power stations, and energy storage power stations respectively. They are the investment decision variables of conventional unit g, transmission line l, wind farm w, photovoltaic power station pv, and energy storage power station SOC respectively, Ω G 、Ω L 、Ω W 、Ω PV 、Ω SOC They are the sets of conventional units, transmission lines, wind farms, photovoltaic power stations, and energy storage power stations respectively, Ω T 、Ω B They are the sets of time and interruptible load respectively. They are the operating costs of conventional unit g, wind farm w, photovoltaic power station pv, and energy storage power station SOC respectively. It is the output of conventional unit g at time t. They are the output of wind farm w at time t and its maximum value respectively. They are the output of photovoltaic power station pv at time t and its maximum respectively. They are the discharge power of energy storage power station SOC and the load shedding amount of interruptible load i at time t respectively. It is the unit power compensation cost of interruptible load i.

[0224] The constraint conditions include decision variable constraints, node power balance constraints, interruptible load user load shedding constraints, energy storage power station capacity constraints, energy storage power station charge and discharge constraints, the relationship constraints between the state of charge of the energy storage and the charge and discharge power, line power flow constraints, conventional unit output constraints, new energy unit output constraints, and node phase angle constraints. Among them, the node power balance constraints are described in the form of chance constraints. The specific constraint conditions are as described in Embodiment 1.

[0225] The power spot market clearing model includes a security-constrained unit commitment sub-model, a security-constrained economic dispatch sub-model, and a power price calculation sub-model. The objective function of the security-constrained unit commitment sub-model includes:

[0226]

[0227] In the above formula, c g,k 、P g,k,t They are the bid price and the winning bid electricity quantity of conventional unit g in segment k respectively. They are the start-up and shutdown costs of conventional unit g at time t respectively, c w,r 、c pv,r They are the bid prices of wind turbine w and photovoltaic power station pv in segment r respectively, P w,r,t 、P pv,r,t They are the winning bid electricity quantities of wind turbine w and photovoltaic power station pv in segment r respectively, Ω G 、ΩL , Ω T , Ω W , Ω PV are the sets of conventional units, transmission lines, time moments, wind farms, and photovoltaic power plants respectively, and Ω Nk , Ω Nr are the sets of the number of price segments of conventional units and new energy units respectively, and C cong is the system congestion cost, is the congestion penalty cost of transmission line l, and s l,t is the congestion variable of transmission line l at time t;

[0228] The constraint conditions include system reserve constraints, upper and lower limits of unit output constraints, unit ramp rate constraints, minimum start-stop time constraints, system power balance constraints, and transmission line power flow constraints. The specific constraint conditions are as described in Embodiment 1.

[0229] The objective function of the security-constrained economic dispatch sub-model includes:

[0230]

[0231] The constraint conditions include system reserve constraints, upper and lower limits of unit output constraints, system power balance constraints, and transmission line power flow constraints. The specific constraint conditions are as described in Embodiment 1.

[0232] The electricity price calculation sub-model is:

[0233]

[0234] In the above formula, y i is the clearing electricity price of the i-th node, is the electricity energy price component of the i-th node, is the congestion price component of the i-th node.

[0235] The upper-layer model solving module is used for:

[0236] Using the hippopotamus optimization algorithm to solve the upper-layer planning model iteratively to obtain the initial source-network-load-storage coordinated planning scheme;

[0237] Iterating according to the clearing price fed back by the lower-layer model to obtain the final source-network-load-storage coordinated planning scheme.

[0238] The solving process of the upper-layer model solving module includes:

[0239] A1. Initialize the hippopotamus population, calculate the fitness function value of each hippopotamus individual in the population, and determine the optimal position and the optimal fitness value. Among them, the fitness function is the objective function of the source-network-load-storage coordinated planning model;

[0240] A2. Calculate the sensitivity of each control variable respectively, determine the proportion of the sensitivity of each control variable, obtain the iteration speed factor of each control variable, and then adjust its initial iteration speed according to the iteration speed factor of each control variable. Among them, the sensitivity of each control variable is calculated according to the following formula:

[0241]

[0242] In the above formula, Δx i and ΔC all are the change amounts of the i-th control variable and the total cost of the power system respectively, is the sensitivity of the i-th control variable;

[0243] The proportion of the sensitivity of each control variable is calculated according to the following formula:

[0244]

[0245] In the above formula, is the proportion of the sensitivity of the i-th control variable, and I is the set of control variables;

[0246] The adjusted initial iteration speed of each control variable is calculated according to the following formula:

[0247]

[0248] In the above formula, is the adjusted initial iteration speed of the i-th control variable, and v0 is the initial iteration speed.

[0249] A3. Update the individual position in turn according to the position update strategy in the hippopotamus exploration stage, predator defense stage, and escape from predator stage;

[0250] A4. Judge whether the iteration termination condition is satisfied. If it is satisfied, output the optimal planning scheme; if not, return to A3 for the next iteration.

[0251] The lower-layer model solving module is used to input the initial source-network-load-storage coordinated planning scheme into the lower-layer clearing model, solve the lower-layer clearing model by using the commercial solver Cplex, and feedback the clearing price to the upper-layer planning model for iterative loop. The specific solving process includes: first solving the security-constrained unit commitment sub-model to obtain the start-stop state of each generating unit, then solving the security-constrained economic dispatch sub-model according to the start-stop state of each generating unit to obtain the output of each generating unit, and finally calculating the clearing electricity price through the electricity price calculation sub-model.

Claims

1. A coordinated planning method for source-grid-load-storage in a power market environment, characterized in that the method includes: S1. Construct a two-layer planning model. The upper layer of the model is a coordinated planning model for source-grid-load-storage that considers the uncertainty of source-load and aims to minimize the total cost of the power system. The lower layer is a power spot market clearing model that aims to minimize the generation cost; S2. Use the hippopotamus optimization algorithm to solve the upper-layer planning model to obtain an initial coordinated planning scheme for source-grid-load-storage; S3. Input the initial coordinated planning scheme for source-grid-load-storage into the lower-layer clearing model and use a commercial solver to solve the lower-layer clearing model to obtain the clearing price; S4. Feed back the clearing price to the upper-layer planning model for iterative calculation to obtain the final coordinated planning scheme for source-grid-load-storage.

2. The coordinated planning method for source-grid-load-storage in a power market environment according to claim 1, characterized in that S2 includes: S21. Initialize the hippopotamus population, calculate the fitness function value of each hippopotamus individual in the population, and determine the optimal position and the optimal fitness value. Among them, the fitness function is the objective function of the coordinated planning model for source-grid-load-storage; S22. Calculate the sensitivity of each control variable respectively, and determine the sensitivity ratio of each control variable, so as to obtain the iterative speed factor of each control variable, and then adjust its initial iterative speed according to the iterative speed factor of each control variable; S23. Update the individual position in turn according to the position update strategies of the hippopotamus exploration stage, predator defense stage, and escaping predator stage; S24. Judge whether the iteration termination condition is satisfied. If it is satisfied, output the optimal planning scheme; if it is not satisfied, return to S23 for the next iteration.

3. The coordinated planning method for source-grid-load-storage in a power market environment according to claim 2, characterized in that the sensitivity of each control variable is calculated according to the following formula: In the above formula, Δx i , ΔC all are the change amounts of the i-th control variable and the total cost of the power system respectively, is the sensitivity of the i-th control variable; the sensitivity ratio of each control variable is calculated according to the following formula: In the above formula, is the sensitivity proportion of the i-th control variable, and I is the set of control variables; the adjusted initial iterative speed of each control variable is calculated according to the following formula: In the above formula, is the adjusted initial iteration speed of the i-th control variable, and v0 is the initial iteration speed.

4. The coordinated planning method for source-grid-load-storage in a power market environment according to claim 1 or 2, characterized in that the objective function of the coordinated planning model for source-grid-load-storage includes: In the above formula, C all is the total cost of the power system, C invest , C op , C DR are the investment cost, operation cost, and compensation cost of the interruptible load respectively, are the investment costs of the state of charge (SOC) of conventional units, transmission lines, wind farms, photovoltaic power stations, and energy storage power stations respectively, are the investment decision variables of conventional unit g, transmission line l, wind farm w, photovoltaic power station pv, and energy storage power station SOC respectively, Ω G , Ω L , Ω W , Ω PV , Ω SOC are the sets of conventional units, transmission lines, wind farms, photovoltaic power stations, and energy storage power stations respectively, Ω T , Ω B are the sets of time and interruptible load respectively, are the operation costs of conventional unit g, wind farm w, photovoltaic power station pv, and energy storage power station SOC respectively, is the output of conventional unit g at time t, are the output of wind farm w at time t and its maximum value respectively, are the output of photovoltaic power station pv at time t and its maximum respectively, are the discharge power of energy storage power station SOC and the load shedding amount of interruptible load i at time t respectively, is the unit power compensation cost of interruptible load i; The constraint conditions include decision variable constraints, node power balance constraints, interruptible load user load shedding constraints, energy storage power station capacity constraints, energy storage power station charge and discharge constraints, the relationship constraint between the state of charge of the energy storage and the charge and discharge power, line power flow constraints, conventional unit output constraints, new energy unit output constraints, and node phase angle constraints. Among them, the node power balance constraint is described in the form of chance constraint.

5. The coordinated planning method for source-grid-load-storage in a power market environment according to claim 1 or 2, characterized in that the power spot market clearing model includes a security-constrained unit commitment sub-model, a security-constrained economic dispatch sub-model, and a electricity price calculation sub-model; the objective function of the security-constrained unit commitment sub-model includes: In the above formula, c g,k and P g,k,t are respectively the bid price and the winning bid electricity quantity of the conventional unit g in segment k, are respectively the start-up and shutdown costs of the conventional unit g at time t, c w,r and c pv,r are respectively the bid prices of the wind turbine w and the photovoltaic power station pv in segment r, P w,r,t and P pv,r,t are respectively the winning bid electricity quantities of the wind turbine w and the photovoltaic power station pv in segment r, Ω G and Ω L and Ω T and Ω W and Ω PV are respectively the sets of conventional units, transmission lines, time, wind farms, and photovoltaic power stations, Ω Nk and Ω Nr are respectively the sets of the number of bid price segments of conventional units and new energy units, C cong is the system congestion cost, is the congestion penalty cost of the transmission line l, s l,t is the congestion variable of the transmission line l at time t; The constraint conditions include system reserve constraints, unit output upper and lower limit constraints, unit ramp rate constraints, minimum start-up and shut-down time constraints, system power balance constraints, and transmission line power flow constraints; The objective function of the security-constrained economic dispatch sub-model includes: The constraint conditions include system reserve constraints, upper and lower limits of generator output constraints, system power balance constraints, and transmission line power flow constraints; The electricity price calculation sub-model is: In the above formula, y i is the clearing electricity price of the i-th node, is the electricity energy price component of the i-th node, is the congestion price component of the i-th node; The S3 includes: first solving the security-constrained unit commitment sub-model to obtain the start-stop states of each generator set, then solving the security-constrained economic dispatch sub-model according to the start-stop states of each generator set to obtain the output of each generator set, and finally calculating the clearing electricity price through the electricity price calculation sub-model.

6. A source-network-load-storage coordinated planning system in a power market environment, characterized in that The system includes a model construction module, an upper-layer model solving module, and a lower-layer model solving module; The model construction module is used to construct a two-layer programming model. The upper layer of this model is a source-network-load-storage coordinated planning model that considers source-load uncertainty and aims to minimize the total cost of the power system, and the lower layer is a power spot market clearing model that aims to minimize the generation cost; The upper-layer model solving module is used for: Using the hippopotamus optimization algorithm to solve the upper-layer planning model iteratively to obtain an initial source-network-load-storage coordinated planning scheme; Iterating according to the clearing price fed back by the lower-layer model to obtain the final source-network-load-storage coordinated planning scheme; The lower-layer model solving module is used to input the initial source-network-load-storage coordinated planning scheme into the lower-layer clearing model, and use a commercial solver to solve the lower-layer clearing model. After obtaining the clearing price, it is fed back to the upper-layer planning model for iterative cycling.

7. A source-network-load-storage coordinated planning system in a power market environment according to claim 6, characterized in that The solution process of the upper-layer model solving module includes: A1. Initialize the hippopotamus population, calculate the fitness function value of each hippopotamus individual in the population, determine the optimal position and the optimal fitness value. Among them, the fitness function is the objective function of the source-network-load-storage coordinated planning model; A2. Calculate the sensitivity of each control variable respectively, and determine the sensitivity ratio of each control variable, so as to obtain the iterative speed factor of each control variable, and then adjust its initial iterative speed according to the iterative speed factor of each control variable; A3. Update the individual position in turn according to the position update strategies of the hippopotamus exploration stage, predator defense stage, and escape from predator stage; A4. Determine whether the iteration termination condition is satisfied. If it is satisfied, output the optimal planning scheme; if not, return to A3 for the next iteration.

8. A source-network-load-storage coordinated planning system in a power market environment according to claim 7, characterized in that The sensitivity of each control variable is calculated according to the following formula: In the above formula, Δx i , ΔC all are respectively the change in the i-th control variable and the total cost of the power system, is the sensitivity of the i-th control variable; The sensitivity ratio of each control variable is calculated according to the following formula: In the above formula, is the sensitivity proportion of the i-th control variable, and I is the set of control variables; The adjusted initial iterative speed of each control variable is calculated according to the following formula: In the above formula, is the adjusted initial iteration speed of the i-th control variable, and v0 is the initial iteration speed.

9. A source-network-load-storage coordinated planning system in a power market environment according to claim 6 or 7, characterized in that The objective function of the source-network-load-storage coordinated planning model includes: In the above formula, C all is the total cost of the power system, C invest , C op , C DR are the investment cost, operating cost, and compensation cost of the interruptible load respectively, are the investment costs of the state of charge (SOC) of conventional units, transmission lines, wind farms, photovoltaic power plants, and energy storage power plants respectively, are the investment decision variables of conventional unit g, transmission line l, wind farm w, photovoltaic power plant pv, and energy storage power plant SOC respectively, Ω G , Ω L , Ω W , Ω PV , Ω SOC are the sets of conventional units, transmission lines, wind farms, photovoltaic power plants, and energy storage power plants respectively, Ω T , Ω B are the sets of time and interruptible load respectively, are the operating costs of conventional unit g, wind farm w, photovoltaic power plant pv, and energy storage power plant SOC respectively, is the output of conventional unit g at time t, are the output of wind farm w at time t and its maximum value respectively, are the output of photovoltaic power plant pv at time t and its maximum respectively, are the discharge power of energy storage power plant SOC and the load shedding amount of interruptible load i at time t respectively, is the unit power compensation cost of interruptible load i; The constraints include decision variable constraints, node power balance constraints, load shedding constraints for interruptible load users, energy storage power station capacity constraints, energy storage charge and discharge constraints, the relationship constraints between the state of charge of the energy storage and the charge and discharge power, line power flow constraints, conventional unit output constraints, new energy unit output constraints, and node phase angle constraints. Among them, the node power balance constraints are described in the form of chance constraints.

10. A source-grid-load-storage coordinated planning system in a power market environment according to claim 6 or 7, characterized in that the power spot market clearing model includes a security-constrained unit commitment sub-model, a security-constrained economic dispatch sub-model, and a price calculation sub-model; the objective function of the security-constrained unit commitment sub-model includes: In the above formula, c g,k , P g,k,t are respectively the bid price and the winning bid electricity quantity of the conventional unit g in section k, are respectively the start-up and shutdown costs of the conventional unit g at time t, c w,r , c pv,r are respectively the bid prices of the wind turbine w and the photovoltaic power station pv in section r, P w,r,t , P pv,r,t are respectively the winning bid electricity quantities of the wind turbine w and the photovoltaic power station pv in section r, Ω G , Ω L , Ω T , Ω W , Ω PV are respectively the sets of conventional units, transmission lines, time, wind farms, and photovoltaic power stations, Ω Nk , Ω Nr are respectively the sets of the number of bid price segments of conventional units and new energy units, C cong is the system congestion cost, is the congestion penalty cost of the transmission line l, s l,t is the congestion variable of the transmission line l at time t; The constraints include system reserve constraints, unit output upper and lower limit constraints, unit ramp rate constraints, minimum start-up and shut-down time constraints, system power balance constraints, and transmission line power flow constraints; the objective function of the security-constrained economic dispatch sub-model includes: The constraints include system reserve constraints, unit output upper and lower limit constraints, system power balance constraints, and transmission line power flow constraints; the price calculation sub-model is: y i = C i system + C i cong In the above formula, y i is the clearing electricity price of the i-th node, is the electricity energy price component of the i-th node, is the congestion price component of the i-th node; The solution process of the lower-layer model solution module is as follows: first, solve the security-constrained unit commitment sub-model to obtain the start-stop states of each generating unit, then solve the security-constrained economic dispatch sub-model according to the start-stop states of each generating unit to obtain the output of each generating unit, and finally calculate the clearing price through the price calculation sub-model.

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