A robust optimization scheduling method for virtual power plant participating in power market

Abstract

The application provides a kind of robust optimization scheduling method of virtual power plant participating in power market, comprising the following steps: under the framework of virtual power plant, construct the mathematical model of battery swap station, energy storage, diesel generator, participating in power market behavior and distribution network voltage, adopt the predicted value and fluctuation value under confidence interval to construct the uncertainty set representing photovoltaic power generation and battery swap demand;With the maximum profit of virtual power plant as objective function, a two-stage robust optimization model is constructed;The two-stage robust optimization model is decomposed into main problem, outer layer of subproblem and inner layer of subproblem by using nested column and constraint generation algorithm, the inner layer of subproblem is added to the constraint of outer layer of subproblem by using Kuhn-Tucker condition, and the scheduling scheme of virtual power plant is obtained by iteration solving through outer and inner loop.The application significantly improves the income and regulation and control ability of virtual power plant in complex power market environment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power market resource scheduling, and particularly relates to a robust optimization scheduling method for a virtual power plant participating in a power market. BACKGROUND

[0002] In recent years, with the rapid development of renewable energy and the increasing popularity of electric vehicles, the flexibility demand of the power market has significantly increased. To this end, domestic and foreign scholars have extensively conducted research on electric vehicle aggregation participating in the power market, mainly focusing on electric vehicle aggregation adjustable capacity evaluation, demand side response, and vehicle-road-network interaction mechanism. However, current research and practical applications still face many technical bottlenecks.

[0003] Among the charging modes of electric vehicles, the plug-in mode is currently the most common way, but it has the following problems:

[0004] The plug-in mode usually uses direct current or alternating current charging piles, which has a significant impact on the power grid, especially during peak load, which can exacerbate the imbalance of the power grid;

[0005] In the vehicle-to-grid (V2G) mode, electric vehicles have high randomness, and there are issues related to user privacy and communication delay, making it difficult to aggregate and regulate.

[0006] In contrast, the battery swap mode, as a new charging method, completes energy interaction with the power grid through a battery swap station, not only meeting the charging needs of users, but also having the following advantages:

[0007] The battery swap mode has higher flexibility and can complete the charging task in a shorter time;

[0008] The battery swap cost is lower, and the battery is centrally managed and maintained by the battery swap operator, which can timely feedback battery state information, thereby reducing the concerns of car owners about battery aging and safety.

[0009] Existing research has conducted related work on the battery swap mode. For example:

[0010] A day-ahead optimization scheduling model for battery swap stations is proposed, focusing on the uncertainty of battery charging demand, time-of-use electricity prices, and constant current / constant voltage charging modes of batteries;

[0011] In the scenario of battery swap stations participating in the power market, some research has proposed bidding strategies for the electricity market and frequency modulation auxiliary service market;

[0012] For heavy truck battery swap scenarios, research has proposed a day-ahead scheduling and real-time correction strategy based on battery swap demand, which can effectively achieve peak shaving of the power grid.

[0013] However, the competitiveness of battery swap stations in participating in the competition of the electricity spot market and the ancillary service market is limited, mainly due to the following reasons:

[0014] The current scale and capacity of the battery swap station are insufficient to meet the efficient operation requirements of the market alone;

[0015] The independently operated battery swap station cannot fully utilize the synergistic effect of multiple energy resources.

[0016] Therefore, by including the battery swap station in the unified management of the virtual power plant and participating in the electricity market as part of the power resource, the market competitiveness and flexibility of the battery swap station can be significantly enhanced. This approach not only provides paid services for the power grid, but also enables the coordinated scheduling of multiple energy sources under the framework of the virtual power plant.

[0017] In terms of grid ancillary services, operating backup is an important means to cope with the intermittency and volatility of renewable energy generation. The virtual power plant participates in grid operation through the power backup market, and needs to fully consider the uncertainty of internal resources (such as the uncertainty of new energy generation and the randomness of load). Existing research has proposed some solutions, such as:

[0018] Modeling the randomness of wind power and backup calls using confidence intervals and confidence limits, and solving a multi-stage robust optimization model through the Column and Constraint Generation (CCG) algorithm;

[0019] Under the two-stage stochastic optimization framework, the uncertainty of electricity prices and calls is handled based on the information gap decision theory.

[0020] Although the above research has made some progress, there are still the following shortcomings:

[0021] Current research mainly focuses on backup service constraints such as minimum backup capacity or duration, and fails to adapt to more complex backup call patterns;

[0022] In more complex backup services (such as the STOR (Short Term Operating Reserve) service in the UK), in addition to requiring minimum aggregated capacity and duration, secondary response capability is also required, which poses higher requirements for the optimal scheduling of virtual power plants;

[0023] Research on complex stochastic call mechanisms is still limited, and most fail to fully consider the uncertainty coupling problem between new energy generation and backup calls. SUMMARY

[0024] The purpose of the present invention is to address the deficiencies in the above-mentioned background technology, and to provide a robust optimization scheduling method for virtual power plants to participate in the electricity market. This method incorporates battery swap stations, energy storage, diesel generators, and photovoltaic power generation into the virtual power plant framework, and constructs an optimized scheduling scheme for participating in the electricity spot market and ancillary service market. This method fully considers the uncertainty of photovoltaic power generation and battery swapping demands through nested columns and constraint generation algorithms, and can achieve reliable scheduling of virtual power plants under complex random call mechanisms. Compared with the existing technology, the present invention significantly improves the revenue and regulation capabilities of virtual power plants in complex electricity market environments.

[0025] The technical solution adopted by the present invention is: a robust optimization scheduling method for a virtual power plant to participate in the power market, comprising the following steps:

[0026] Within the framework of a virtual power plant, a mathematical model of battery swap stations, energy storage, diesel generators, electricity market participation, and distribution network voltage is constructed. The predicted values ​​and fluctuation values ​​within confidence intervals are used to construct an uncertainty set representing photovoltaic power generation and battery swapping demand.

[0027] Among them, virtual power plants aggregate battery swap stations, energy storage, photovoltaics and diesel generators as power resources, and participate in the power spot and ancillary service markets as power trading entities;

[0028] Based on the above mathematical model, with virtual power plant profit maximization as the objective function, a two-stage robust optimization model for virtual power plants participating in the electricity market under multiple uncertainties is constructed. The uncertainty set is used as a constraint in the optimization model to represent the uncertainty of photovoltaic power generation and battery swapping demand.

[0029] A nested column and constraint generation algorithm is used to decompose the two-stage robust optimization model into a main problem, an outer subproblem layer, and an inner subproblem layer. The inner subproblem layer is then added to the constraints of the outer subproblem layer using Kuhn-Tucker conditions. The solution is then iterated through outer and inner loops to obtain a virtual power plant scheduling solution.

[0030] The scheduling plan includes: electricity sales allocation in different time periods; spare capacity allocation in different time periods; diesel generator start and stop status in different time periods; optimal charging and discharging strategies for energy storage and battery swap stations; and optimal values ​​for standby call, photovoltaic power generation, and battery swap requirements.

[0031] In the technical solution, the mathematical model of the battery swap station is used to represent: the charging power and discharging power limits of the battery swap station; the battery swap station can only be in the charging, discharging or static state at any time step; the total energy stored in the battery swap station; the energy change of the battery swap station at each time step according to the charging and discharging operation meets the charging and discharging efficiency; the relationship between the increase or decrease of the energy of the battery swap station in the charging and discharging process and the number of batteries to be swapped in the battery swap station; the constraint relationship between the number of at least reserved batteries and the energy of a single full battery in the battery swap station and the total energy of the battery swap station and the maximum rated energy of the generalized energy storage of the battery swap station; the initial energy of the battery swap station in the optimization period is consistent with the maximum rated energy of the generalized energy storage of the battery swap station.

[0032] In the technical solution, the mathematical model of the energy storage is used to represent: the charging and discharging power limits of the energy storage system; the energy storage system can only be in the charging, discharging or static state at any time step; the energy of the energy storage system changes at each time step according to the charging and discharging operation, which meets the charging and discharging efficiency; the energy range constraint of the energy storage system; the energy of the energy storage system at the end time is consistent with the initial state, which is half of the maximum rated energy of the energy storage.

[0033] In the technical solution, the mathematical model of the diesel engine is used to represent: the power generation power constraint of the diesel generator; the start-stop state constraint of the diesel generator.

[0034] In the technical solution, the mathematical model of the participation in the power market behavior is used to represent: the sum of the power selling amount and the standby calling amount of the virtual power plant is equal to the net injection power of all devices.

[0035] In the technical solution, the mathematical model of the distribution network voltage is used to represent: the power balance of each node; the active power and reactive power flow of the line between nodes; the voltage difference relationship between nodes; the relationship between the current flow of the line and the node voltage; the range of the amplitude of the node voltage.

[0036] In the technical solution, the target of the first stage of the optimization model is to maximize the revenue of the virtual power plant in the spot market and the standby market minus the related cost;

[0037] The target of the second stage is to minimize the system operation cost including the standby calling, the diesel generator start-up cost, the energy storage and the battery swap station operation cost under the condition of uncertainty;

[0038] The decision variables of the first stage include the standby capacity submitted to the standby market, the power sold by the virtual power plant to the spot market, and the start-stop state of the diesel generator;

[0039] The uncertainty decision variables of the second stage include: the calling of the standby by the power grid, the actual value of the photovoltaic power generation, the actual value of the battery swap energy, the up and down adjustment coefficients of the photovoltaic power generation and the average energy of the batteries to be swapped.

[0040] The state decision variables of each device in the second stage include: power generation of the diesel generator, charging and discharging power of the energy storage, charging and discharging state variables of the energy storage, energy of the energy storage, charging and discharging power of the battery swap station, charging and discharging state variables of the battery swap station, total energy of the battery swap station, and node voltage.

[0041] In the technical solution, the main problem maximizes the total profit of the virtual power plant on the basis of the first-stage decision variables.

[0042] In the technical solution, the outer layer of the sub-problem determines the worst case of the photovoltaic power station, the battery swap station and the standby call according to the optimal value of the main problem and the charging and discharging binary variable scene, and minimizes the total cost of the virtual power plant in the worst case.

[0043] In the technical solution, the inner layer of the sub-problem determines the output of the battery swap station, the energy storage, the photovoltaic power station and the diesel generator, and the charging and discharging binary variables of the battery swap station and the energy storage system according to the worst case obtained by the outer layer of the sub-problem, so as to maximize the income or minimize the cost.

[0044] The present application has the advantages that: the present application aggregates various energy resources such as the battery swap station, the energy storage, the photovoltaic power station and the diesel generator by constructing a virtual power plant framework, and improves the system scheduling flexibility. The uncertain set of the photovoltaic power station and the battery swap demand is constructed, and is embedded in the optimization model in the form of a constraint condition, so as to enhance the adaptability of the virtual power plant to the new energy fluctuation and the change of the power demand. The two-stage robust optimization model is taken as the core, and the decomposition solving of the main problem and the sub-problem is combined, so as to optimize the income and the cost of the virtual power plant under the uncertainty. The scheduling scheme covers the power distribution, the standby capacity distribution, the start-stop state of the diesel generator, and the charging and discharging strategy of the energy storage and the battery swap station, and provides an executable optimization result for the actual operation.

[0045] Further, the mathematical model of the battery swap station of the present application characterizes the charging power limit, the charging and discharging state and the energy change of the battery swap station in detail, so as to ensure that the battery swap station operation meets the physical constraints. By introducing the constraint relationship between the number of battery swap batteries and the energy of a single battery and the total energy, the charging and discharging strategy of the battery swap station can be dynamically adjusted to meet the actual demand. The model takes into account the influence of the charging and discharging efficiency, which helps to improve the energy utilization efficiency. By setting the initial energy of the battery swap station to be consistent with the maximum rated energy, the continuity and stability of the battery swap station operation in the optimization period are ensured.

[0046] Further, the mathematical model of the energy storage of the present application ensures that the energy storage operates within a safe range by limiting the charging and discharging power and state of the energy storage system. The model characterizes the energy change of the energy storage system at each time step and its range constraints, supporting precise energy dynamic management. Setting the terminal energy of the energy storage system consistent with the initial state provides long-term balance and controllability for the energy storage. Considering the impact of charging and discharging efficiency on energy change helps to improve the economy of the energy storage system.

[0047] Further, the mathematical model of the diesel generator of the present application ensures that the output power of the diesel generator meets the physical and economic requirements through power generation constraints. The start-stop state constraints can dynamically adjust the operation mode of the diesel generator, reduce unnecessary start-up and shutdown losses, and improve operation efficiency. As a backup power source, the diesel generator can provide stable power output in emergency situations, improving the reliability of the system.

[0048] Further, the mathematical model of the participation in the power market behavior of the present application ensures the overall power balance of the virtual power plant by the constraint that the amount of electricity sold and the amount of standby calling are equal to the net injection power of all devices. The model can balance the participation strategies of the spot market and the standby market, improving the revenue of the virtual power plant. The power output of all devices is coordinated within one framework, realizing the collaborative optimization of multiple energies.

[0049] Further, the mathematical model of the distribution network voltage of the present application ensures the voltage stability of the distribution network operation through the voltage difference relationship between nodes and the node voltage amplitude range constraint. The active and reactive power flow relationship of the line is described, providing accurate power scheduling basis for the operation of the distribution network. The relationship constraint between current flow and node voltage can prevent line overload, improving the safety and reliability of the distribution network operation.

[0050] Further, the two-stage robust optimization model of the present application has the first stage as the target of maximizing revenue and the second stage as the target of minimizing cost, clearly defines the optimization direction, and improves the overall benefit of the virtual power plant; covers multiple variables such as standby capacity, electricity sales, grid calling, and device state, providing a comprehensive scheduling optimization scheme; the optimization of uncertain variables (such as photovoltaic power generation and battery replacement demand) improves the ability of the virtual power plant to cope with new energy fluctuations and load uncertainty.

[0051] Further, the main problem of the present application directly improves the economic benefit of the virtual power plant by optimizing the first-stage decision variables. The complex multi-stage problem is decomposed into main problem and sub-problem for solving, reducing the complexity of solving. At the same time, considering the revenue of the spot market and the standby market, the economy of the two is comprehensively balanced.

[0052] Further, the outer layer of the sub-problem of the application aims to minimize the cost in the worst case, improving the robustness of the virtual power plant scheduling scheme. By determining the worst case of photovoltaic, battery swap station and standby call, the reliability and applicability of the optimization scheduling strategy are optimized. According to the optimal value of the main problem, further optimization is carried out, which guarantees the coordination of problem decomposition and the effectiveness of solution.

[0053] Further, the worst case obtained by the outer layer of the sub-problem of the application is optimized to determine the specific output of the battery swap station, energy storage, photovoltaic and diesel generator, and improve the accuracy of scheduling. The charging and discharging behavior of the battery swap station and the energy storage system is optimized, which enhances the adaptability of the virtual power plant to new energy fluctuations. Through dynamic optimization of equipment operation state, the goal of maximizing benefit or minimizing cost is realized. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The virtual power plant participates in the overall framework of the power market;

[0055] Figure 2 The virtual power plant two-stage robust optimization scheduling framework based on the nested column and constraint generation algorithm of the application;

[0056] Figure 3 The virtual power plant distribution diagram under the 6-node distribution network in the embodiment;

[0057] Figure 4 The iterative convergence process of the nested column and constraint generation algorithm in the embodiment;

[0058] Figure 5 The standby amount, standby call power and power selling amount diagram of the virtual power plant in the embodiment;

[0059] Figure 6 The operation state of each asset of the virtual power plant under different uncertainty combinations in the embodiment;

[0060] Figure 7 The distribution network daily voltage distribution diagram under different uncertainty combinations in the embodiment. DETAILED DESCRIPTION

[0061] The application will be further described in detail below with reference to the accompanying drawings and specific embodiments, so as to facilitate clear understanding of the application, but they do not constitute limitation to the application.

[0062] Embodiment 1

[0063] As shown in Figure 1 The application provides a robust optimization scheduling method for virtual power plant participating in power market, which comprises the following steps:

[0064] Within the framework of a virtual power plant, a mathematical model of battery swap stations, energy storage, diesel generators, electricity market participation, and distribution network voltage is constructed. The predicted values ​​and fluctuation values ​​within confidence intervals are used to construct an uncertainty set representing photovoltaic power generation and battery swapping demand.

[0065] Among them, virtual power plants aggregate battery swap stations, energy storage, photovoltaics and diesel generators as power resources, and participate in the power spot and ancillary service markets as power trading entities;

[0066] Based on the above mathematical model, with virtual power plant profit maximization as the objective function, a two-stage robust optimization model for virtual power plants participating in the electricity market under multiple uncertainties is constructed. The uncertainty set is used as a constraint in the optimization model to represent the uncertainty of photovoltaic power generation and battery swapping demand.

[0067] A nested column and constraint generation algorithm is used to decompose the two-stage robust optimization model into a main problem, an outer subproblem layer, and an inner subproblem layer. The inner subproblem layer is then added to the constraints of the outer subproblem layer using Kuhn-Tucker conditions. The solution is then iterated through outer and inner loops to obtain a virtual power plant scheduling solution.

[0068] The scheduling plan includes: electricity sales allocation in different time periods; spare capacity allocation in different time periods; diesel generator start and stop status in different time periods; optimal charging and discharging strategies for energy storage and battery swap stations; and optimal values ​​for standby call, photovoltaic power generation, and battery swap requirements.

[0069] This embodiment specifically includes the following steps:

[0070] Step 1: Based on the short-term operating reserve requirements, construct a reserve call uncertainty set; use the predicted values ​​and fluctuation values ​​under the confidence interval to represent the uncertainty of battery swapping demand and photovoltaic power generation, and form a corresponding uncertainty set.

[0071] As shown in Table 1, short-term operating reserve requirements set minimum aggregate capacity, duration, and secondary response capability:

[0072] Table 1 UK STOR backup service technical indicators

[0073]

[0074] Based on the specific technical indicators, the uncertainty of standby call is modeled and the corresponding uncertainty set is obtained as follows:

[0075]

[0076] [d t ,d t+1 ,...,d t+n ]≥1-M d [1-(dt -d t-1 )] (1b)

[0077]

[0078] In the above formula, Λ d represents the uncertain set of reserve calls, n and m correspond to the duration of reserve calls and the secondary response time respectively, d t is the grid call time. Equation (1b) constrains the duration of the reserve call, and the big M method is used to represent the logical judgment relationship. When t-1 is not called and t is called, d t is 1 in the time range from t to t+n; equation (1c) constrains the secondary response time of the reserve, when t-1 is called to 1 and t is called to 0, then d t is 1 in the time range from t to t+m, indicating that the virtual power plant has the ability to accept calls again in this period.

[0079] The uncertainty of the battery replacement demand and photovoltaic power generation is represented by the predicted value and the fluctuation value under the confidence interval. The uncertain set Λ r,s is as follows:

[0080]

[0081] In the above formula, Λ r,s represents the uncertain set of photovoltaic power generation and battery replacement demand, k t s+ and are the upper and lower adjustment coefficients of the average energy of photovoltaic power generation and battery to be replaced, and are binary variables; P t r , P t r,～ and ΔP t r are the actual value, predicted value and fluctuation value of photovoltaic power generation respectively; and are the actual value, predicted value and fluctuation value of the battery replacement battery energy respectively; Γ r and Γ s are the uncertainty of photovoltaic power generation and battery replacement demand. Equation (2b) and (2c) represent the actual value of photovoltaic power generation and battery replacement demand in the worst case, equation (2d) constrains that the upper and lower fluctuations cannot appear at the same time, equation (2e) limits the size of uncertainty, and reduces the conservatism of the optimization result. When Γ r = 0 or Γ s = 0, the actual value is equal to the predicted value, the greater the uncertainty coefficient, the greater the deviation of the actual value compared with the predicted value.

[0082] Step 2: Construct a virtual power plant participating in the electricity market framework, which includes battery swap stations, energy storage, diesel generators, and photovoltaics as virtual power plant assets; in turn, mathematical modeling is performed on battery swap stations, energy storage, diesel generators, participation in electricity market behavior, and distribution network voltage models.

[0083] The virtual power plant participating in the electricity market framework is shown in Figure 1 , which aggregates resources such as battery swap stations, energy storage, photovoltaics, and diesel generators to participate in the electricity spot and ancillary service markets as a power trading subject. The virtual power plant includes battery swap stations in its management, indirectly realizing the aggregation and regulation of electric vehicles; the diesel generator provides stable power output, and the energy storage shifts new energy generation in the time dimension. In addition to selling surplus electricity in the spot market, the virtual power plant provides backup services for the grid through the ancillary service market, and the backup capacity that can be provided is determined in the day-ahead, so that the random call in the day-ahead is fully responded to.

[0084] The mathematical models of battery swap stations, energy storage, diesel generators, participation in electricity market behavior, and distribution network voltage models are as follows:

[0085] (1) Mathematical model of electric vehicle battery swap station:

[0086]

[0087] In the formula, subscript t represents the time, and are the charging and discharging power of the battery swap station, and are the charging and discharging state variables of the battery swap station, p s,max is the maximum power of the battery swap station, is the total energy of the battery swap station, is the energy change caused by the battery swap station, is the charging and discharging efficiency of the battery swap station, Δt is the charging and discharging time step, N min is the number of at least reserved batteries in the battery swap station, E max is the energy of a single fully charged battery, E s,max is the maximum rated energy of the battery swap station's generalized energy storage, N t is the number of batteries currently to be replaced, is the average energy of these batteries, is the initial energy of the battery swap station in the optimization period. Formulas (3a)-(3d) are the charging and discharging power constraints of the battery swap station, which do not allow simultaneous charging and discharging to occur; formula (3e) represents the generalized energy storage energy change of the battery swap station; formula (3f) represents the minimum and maximum energy constraints of the battery swap station, which requires the battery swap station to reserve a certain number of fully charged batteries; formula (3g) represents the energy change caused by the battery swap station, and formula (3h) gives the initial energy of the battery swap station.

[0088] (2) Mathematical model of energy storage:

[0089]

[0090] Similar to the generalized energy storage of battery swap station, are the charging and discharging power of energy storage, are the charging and discharging state variables of energy storage, p b,max are the maximum charging and discharging power of energy storage, is the energy of energy storage, are the charging and discharging efficiency of energy storage, E b,max is the maximum rated energy of energy storage, soc min , soc max are the minimum and maximum state of charge of energy storage, are the initial state and terminal state of energy storage energy in simulation time domain; it is required that the energy at the terminal time is consistent with the initial state.

[0091] (3) Mathematical model of diesel generator:

[0092]

[0093]

[0094] In the above formula, and are the power generation of diesel generator and the minimum and maximum power generation, respectively; indicates the start-stop state of diesel generator; a large M constant indicates MG. Equation (5a) indicates that the power output of diesel generator is maintained within a given range after starting; equation (5b) indicates that the diesel generator needs to maintain the start state for at least l steps after starting; and equation (5c) constrains the shutdown duration.

[0095] (4) Power balance of virtual power plant participating in power market:

[0096]

[0097] Considering that the virtual power plant participates in both the power spot market and the reserve service market, equation (6) gives the power balance of the virtual power plant participating in the power market, i.e., the sum of the power sold and the reserve called by the virtual power plant is equal to the net injection power of all devices. Wherein is the power sold by the virtual power plant to the power spot market; R t is the reserve capacity submitted to the reserve market in advance, d t is the calling of reserve by the power grid in real time, which is assumed to be d t ∈{0, 1}, i.e., once the power grid calls the reserve, it is fully used.

[0098] (5) Voltage model of distribution network:

[0099]

[0100] In the above formula, if there is no diesel engine at busbar i, then The same goes for the power of energy storage and battery swap stations; is the active and reactive power of the load at bus i; N is the set of distribution network buses, N j is the set of buses connected to bus j; p ij,t ,q ij,t and I ij are the active and reactive power flows and the square of the current amplitude of line ij, respectively, v i,t is the square of the node voltage amplitude, α ij is the convexity coefficient, V min and V max are the minimum and maximum allowable voltage amplitudes. When participating in the electricity spot and reserve markets, virtual power plants must consider the voltage constraints of the distribution network. Equation (7a) represents the injected power at the node, (7b)-(7f) are the power flow models for the radial distribution network, and Equation (7e) gives the convexified relationship between voltage and current. Equation (7f) ensures that virtual power plant scheduling does not cause the voltage of its supporting grid to exceed the limit.

[0101] Step 3: Construct a two-stage robust optimization model for virtual power plants participating in the electricity market under multiple uncertainties. The optimization model takes maximizing virtual power plant profits as the objective function, and the constraints include the operation constraints of various power resources, power balance constraints, and distribution network voltage constraints. Under multiple uncertainties, the first stage of the two-stage robust optimization model determines the day-ahead power sales, reserve capacity, and diesel generator start and stop status. The second stage determines the operating output of each device under the known worst-case scenario. The model is shown below:

[0102]

[0103] coefficient and They correspond to reserve income, electricity sales income, diesel generator startup costs, reserve call income, diesel generator operating costs, energy storage operating costs, and battery swap station operating costs respectively; is the relative start / stop status of the diesel generators at time t. The objective function is to maximize the profit of the virtual power plant participating in the electricity spot and reserve markets under multiple uncertainties. The constraints are the operating constraints of the virtual power plant's internal assets, the diesel generator start / stop expressions, and the maximum reserve and electricity sales capacity that the virtual power plant can provide. X is the set of decision variables in the first stage, U is the set of uncertainty decision variables in the second stage, and Y is the set of state decision variables for each device in the second stage.

[0104] Step 4: The two-stage robust optimization model is decomposed into master problem, outer layer of sub-problem and inner layer of sub-problem by using nested column-and-constraint generation algorithm, and the inner layer of sub-problem is added to the constraints of outer layer of sub-problem by using Karush-Kuhn-Tucker (KKT) condition, and the outer and inner loops are iterated to solve.

[0105] In step 4, the two-stage robust model is decomposed into master problem and sub-problem and iterated to solve by using CCG algorithm framework. Considering that the inner layer of sub-problem contains binary variables of energy storage and battery swap station, the double-layer sub-problem cannot be converted into single-layer problem by using Karush-Kuhn-Tucker (KKT) condition. Therefore, the nested column-and-constraint generation algorithm is used here, which gives the upper and lower bounds of the optimal value by the master problem and the sub-problem respectively, and the constraints and decision variables under specific scenarios are constantly added in the master problem, and the master problem and the sub-problem are iterated to optimize and calculate until the upper and lower bounds converge to approximate values. The nested column-and-constraint generation algorithm divides the sub-problem into outer layer and inner layer, and the binary variables in the inner layer are expressed as scenarios in the outer layer of the sub-problem, and then the KKT condition is used to convert the inner layer of the sub-problem into the constraint condition of the outer layer, and then the inner and outer layers are iterated to solve, and the nested column-and-constraint generation algorithm framework is shown in Figure 2 .

[0106] (1) Master problem model:

[0107]

[0108]

[0109] (3b), (3c), (6c), (6d) (9b)

[0110]

[0111]

[0112]

[0113] It is the decision variable set of the master problem; the scale of the master problem is constantly increasing with the iteration number k, and the auxiliary variable θ of the cut plane is used to constantly iterate and approximate the worst case of the sub-problem. Wherein, The optimal values of the call, photovoltaic power generation and battery swap demand obtained by the k'th iteration of the sub-problem are respectively. Wherein, the superscript or subscript k' represents the value represented by the corresponding character obtained by the k'th iteration of the sub-problem.

[0114] (2) Outer layer of sub-problem:

[0115]

[0116] KKT (Formula 11a-11f) (10c)

[0117]

[0118]

[0119] ,

[0120] It is the set of decision variables in the outer layer of the subproblem. The size of the outer layer of the subproblem increases with the number of iterations kk, and the worst case of the inner layer of the subproblem is approached through the auxiliary variable η of the cutting plane. The optimal values ​​of the binary variables for the charging and discharging states of the energy storage and battery swap stations are obtained by iteratively solving the inner layer of the subproblem kk'. The superscript or subscript kk' denotes the value represented by the corresponding character obtained by iteratively solving the inner layer of the subproblem kk'.

[0121] (3) Inner layer of sub-problems:

[0122]

[0123] In the above formula, and The electricity sales, reserve capacity and start / stop status of the diesel generator obtained in the kth iteration of the main problem are respectively, and The optimal values ​​of standby call, photovoltaic power generation, and battery swapping demand obtained at the kkth iteration of the outer subproblem are used as known quantities in the inner subproblem. The superscript or subscript kk represents the value represented by the corresponding character obtained by solving the kkth iteration of the outer subproblem.

[0124] Based on the decomposed main problem, outer subproblem layer, and inner subproblem layer, the specific process of the nested column and constraint generation algorithm is as follows:

[0125] (1) Set the inner and outer robust bounds, convergence accuracy, and iteration coefficients for the main problem and subproblems. Outer loop: Set the initial upper and lower bounds (UB, LB) UB = +∞, LB = -∞, Δ = 0.05, k = 1; Inner loop: inUB = +∞, inLB = -∞, inΔ = 0.05, kk = 1. Main problem scenario set Subproblems

[0126] (2) Given the main problem of photovoltaic power generation Battery replacement demand and the initial worst-case scenario of the call Energy storage in a given subproblem and battery swap stations Initial scenario of binary variables of charge and discharge.

[0127] (3) Adding photovoltaic power generation to the main problem scenario set MPS Battery replacement demand and the initial worst-case scenario of the call

[0128] (4) Based on MPS, by optimizing the objective function of the main problem, the optimal first-stage decision variables such as power sales, backup capacity and diesel generator start-stop status are obtained. Specifically, solve the main problem MP (9a)-(9z), the objective function value is MP*, update the outer loop upper limit UB = MP*, and obtain X in turn. *,k : Optimal electricity sales value Reserve and diesel generator start and stop values

[0129] (5) Adding energy storage to the sub-problem scenario set SPS and battery swap stations Scenario of binary variables.

[0130] (6) Results X based on SPS and the main problem *,k , solve SMP(10a)-(10y), the objective function value is SMP*, update the inner loop lower limit inLB=SMP*, and get U *,kk :Generate the worst-case call backup in the outer objective function Photovoltaic power generation and the need for battery replacement worst-case scenario.

[0131] (7) The worst-case scenario result U based on the outer layer *,kk Through the KKT condition, the optimization problem in the inner layer of the subproblem is simplified to solving the gradient condition and complementary relaxation condition of the Lagrangian function. The charging and discharging strategies of energy storage and battery swap stations are optimized in the inner layer of the subproblem, and a binary variable scenario is generated:

[0132] Solve SSP (11a)-(11f), the objective function value is SSP*, optimize the charging and discharging strategy of energy storage and battery swap station in the inner layer of the sub-problem, and generate binary variable scenarios. Update the inner loop upper limit inUB = max (inUB, SSP*), and get energy storage and battery swap stations Charge and discharge binary variables.

[0133] In the energy change formula of the energy storage system and the battery swap station, the KKT condition can help deal with the relationship between energy balance and charging and discharging power. The KKT condition ensures that the charging and discharging power of the battery swap station and energy storage will not exceed the maximum rated power; through the complementary relaxation condition, the KKT condition can ensure that the battery swap station and energy storage can only be in charging, discharging or static state in each time step.

[0134] By applying KKT conditions, the optimal solution of the inner subproblem (such as charging and discharging power, energy state, etc.) can generate a set of linearized constraints. These constraints are fed back to the outer subproblem (SMP) to adjust the worst-case scenario of the outer subproblem's uncertain variables (such as photovoltaic power generation and battery replacement requirements).

[0135] (8) If Then the subproblem iteration ends, θ * =(SMP*+SSP*) / 2, update the lower limit of the outer loop Otherwise, kk=kk+1, jump to step (5).

[0136] (9) If The algorithm ends; otherwise, jump to step (3), k=k+1.

[0137] Step 5: A simulation example is designed to verify the effectiveness of the robust control method of the virtual power plant in participating in the power reserve market and spot market under multiple uncertainties.

[0138] In step 5, a simulation example of a 6-node distribution network is designed, taking into account the distribution network frame and voltage constraints, such as Figure 3 As shown in the figure, a diesel generator set is set at bus 1, a battery swap station is set at bus 2, and a photovoltaic and energy storage unit group is set at bus 4. Three uncertainty combinations are set in the simulation experiment, namely Tr, Te = {0, 0}, {3, 3}, and {7, 5}.

[0139] First, the convergence of the nested column and constraint generation algorithm is verified, as Figure 4 As shown in the figure, both the inner and outer loops of the algorithm converge quickly within 2 iterations. Figure 5 The study presents the reserve capacity, power sales, and worst-case reserve call scenarios of virtual power plants under different uncertainty combinations. The reserve capacity provided by the virtual power plant is always greater than the lower limit of 0.5 MW, meeting the random call requirement. Power sales and reserve call scenarios are concentrated between 10:00 AM and 3:00 PM, when photovoltaic power generation is high and the virtual power plant has a large surplus of power. The reserve capacity provided by the virtual power plant is capped at 1.5 MW between 0:00 AM and 3:00 PM, and between 10:00 PM and 11:00 PM. During the peak load period between 3:00 PM and 10:00 PM, the reserve capacity varies with the uncertainty of photovoltaic power generation and battery swapping.Figure 6 The output of each asset in the virtual power plant and the change of the SoC of the energy storage are given. The diesel generator provides stable power output throughout the day, the energy storage system mainly absorbs photovoltaic power generation, and the battery swap station meets the battery swap demand while participating in the virtual power plant power sales and providing backup capacity. Figure 7 The distribution network voltage is shown, and the voltage of each bus is controlled within a given range.

[0140] The final simulation results show that the proposed virtual power plant framework and the corresponding robust control method are real and effective. The results obtained by simulating the method meet the requirements of short-term operation backup and can effectively promote the virtual power plant to participate in power market transactions.

[0141] Embodiment 2

[0142] The present application provides a robust optimization scheduling system for a virtual power plant participating in a power market, comprising:

[0143] A mathematical model and an uncertainty set construction module, under the virtual power plant framework, constructs mathematical models of battery swap stations, energy storage, diesel generators, participation in power market behavior and distribution network voltage, and uses the predicted values and fluctuation values under the confidence interval to construct the uncertainty set representing photovoltaic power generation and battery swap demand; wherein the virtual power plant aggregates the battery swap station, the energy storage, the photovoltaic and the diesel generator as a power resource, and participates in the power spot and auxiliary service market as a power trading subject;

[0144] An optimization model construction module, based on the above mathematical model, constructs a two-stage robust optimization model for the virtual power plant participating in the power market with the maximum profit of the virtual power plant as the objective function under multiple uncertainties; wherein the uncertainty set is used as a constraint condition in the optimization model to represent the uncertainty of photovoltaic power generation and battery swap demand;

[0145] An optimization model solving module, which decomposes the two-stage robust optimization model into a main problem, an outer layer of a sub-problem and an inner layer of a sub-problem by using a nested column and constraint generation algorithm, adds the inner layer of the sub-problem to the constraints of the outer layer of the sub-problem by using the Kuhn-Tucker condition, and iteratively solves through outer and inner loops to obtain a scheduling scheme of the virtual power plant;

[0146] The scheduling scheme includes: power sales allocation in different time periods; backup capacity allocation in different time periods; start-stop state of the diesel generator in different time periods; optimal charging and discharging strategy of the energy storage and the battery swap station; optimal values of backup calling, photovoltaic power generation and battery swap demand.

[0147] Embodiment 3

[0148] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the robust optimization scheduling method of a virtual power plant participating in an electricity market.

[0149] Embodiment 4

[0150] The application further provides an electronic device, which comprises a memory and a processor, the memory and the processor are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to realize the robust optimization scheduling method of a virtual power plant participating in an electricity market.

[0151] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product in the form of being implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0152] The application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for realizing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The devices for realizing the functions specified in one or more flows and / or blocks.

[0153] These computer program instructions can also be stored in a computer readable storage medium, which can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices, which realize the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The devices for realizing the functions specified in one or more flows and / or blocks.

[0154] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer implemented process, so that the instructions executed on the computer or other programmable data processing devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0155] The embodiments of the present application are described above with reference to the accompanying drawings, but the present application is not limited to the specific embodiments described above, and the specific embodiments described above are merely illustrative, but not restrictive, and a person of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which all belong to the protection of the present application.

[0156] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

Claims

1. A robust optimization scheduling method for virtual power plants participating in the electricity market, characterized by: The following steps are involved: Within the framework of a virtual power plant, a mathematical model of battery swap stations, energy storage, diesel generators, electricity market participation, and distribution network voltage is constructed. The predicted values ​​and fluctuation values ​​within confidence intervals are used to construct an uncertainty set representing photovoltaic power generation and battery swapping demand. Among them, virtual power plants aggregate battery swap stations, energy storage, photovoltaics and diesel generators as power resources, and participate in the power spot and ancillary service markets as power trading entities; Based on the above mathematical model, with virtual power plant profit maximization as the objective function, a two-stage robust optimization model for virtual power plants participating in the electricity market under multiple uncertainties is constructed. The uncertainty set is used as a constraint in the optimization model to represent the uncertainty of photovoltaic power generation and battery swapping demand. A nested column and constraint generation algorithm is used to decompose the two-stage robust optimization model into a main problem, an outer subproblem layer, and an inner subproblem layer. The inner subproblem layer is then added to the constraints of the outer subproblem layer using Kuhn-Tucker conditions. The solution is then iterated through outer and inner loops to obtain a virtual power plant scheduling solution. The scheduling plan includes: electricity sales allocation in different time periods; spare capacity allocation in different time periods; diesel generator start and stop status in different time periods; optimal charging and discharging strategies for energy storage and battery swap stations; and optimal values ​​for spare call, photovoltaic power generation, and battery swap requirements. The goal of the first stage of the optimization model is to maximize the revenue of the virtual power plant in the spot market and the reserve market minus the related costs; The goal of the second phase is to minimize system operating costs, including backup calls, diesel generator startup costs, energy storage, and battery swap station operating costs, under uncertain conditions. The decision variables in the first stage include the reserve capacity submitted to the reserve market, the amount of electricity sold by the virtual power plant to the spot market, and the start and stop status of the diesel generator; The uncertainty decision variables in the second stage include: the grid's call for backup, the actual value of photovoltaic power generation, the actual value of the energy of the battery to be replaced, and the upward and downward adjustment coefficients of the average energy of photovoltaic power generation and the battery to be replaced; The state decision variables of each device in the second stage include: the power generation power of the diesel generator, the charging and discharging power of the energy storage, the charging and discharging state variables of the energy storage, the energy storage energy, the charging and discharging power of the battery swap station, the charging and discharging state variables of the battery swap station, the total energy of the battery swap station, and the node voltage.

2. The method according to claim 1, wherein: The mathematical model of the battery swap station is used to characterize: the limitations of the charging and discharging power of the battery swap station; the battery swap station can only be in the charging, discharging or static state in any time step; the total energy stored in the battery swap station; the energy change of the battery swap station in each time step according to the charging and discharging operations, which complies with the charging and discharging efficiency; the relationship between the increase or decrease of energy in the battery swap station during the charging and discharging process and the number of batteries to be swapped in the battery swap station; the constraint relationship between the minimum number of batteries retained in the battery swap station and the energy of a single fully charged battery and the total energy of the battery swap station and the maximum rated energy of the generalized energy storage of the battery swap station; the initial energy of the battery swap station in the optimization period is consistent with the maximum rated energy of the generalized energy storage of the battery swap station.

3. The method according to claim 1, wherein: The mathematical model of energy storage is used to characterize: the energy storage system's charge and discharge power limits; the energy storage system can only be in a charging, discharging, or static state within any time step; the energy of the energy storage system changes within each time step based on the charging and discharging operations, consistent with charge and discharge efficiency; and the energy range constraints of the energy storage system; The energy of the energy storage system at the end moment is consistent with the initial state, which is half of the maximum rated energy of the energy storage.

4. The method according to claim 1, wherein: The mathematical model of the diesel generator is used to characterize: the power generation constraint of the diesel generator; and the start / stop state constraint of the diesel generator.

5. The method according to claim 1, wherein: The mathematical model of the electricity market participation behavior is used to characterize that the sum of the power sales and reserve call volume of the virtual power plant is equal to the net injected power of all equipment.

6. The method according to claim 1, wherein: The mathematical model of the distribution network voltage is used to characterize: the power balance of each node; the flow of active power and reactive power in the lines between nodes; the voltage difference relationship between nodes; the relationship between the current flow of the line and the node voltage; and the range of constrained node voltage amplitudes.

7. The method according to claim 1, wherein: The main problem is to maximize the total profit of the virtual power plant based on the decision variables in the first stage.

8. The method according to claim 7, wherein: The outer layer of the sub-problem determines the worst case scenario of photovoltaic, battery swap and backup call based on the optimal value of the main problem and the charging and discharging binary variable scenario, and minimizes the total cost of the virtual power plant in the worst case scenario.

9. The method according to claim 8, characterized in that: The inner layer of the sub-problem determines the output of the battery swap station, energy storage, photovoltaic and diesel generators, and the charging and discharging binary variables of the battery swap station and energy storage system based on the worst case scenario obtained by the outer layer of the sub-problem to maximize benefits or minimize costs.

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