Load side flexibility resource optimal configuration method and system based on robust optimization

Abstract

The invention discloses a load side flexibility resource optimization configuration method and system based on robust optimization, and the method comprises the steps: determining the upper and lower bounds of the net load deviation at the current moment according to the load power and distributed power supply power at the current moment; determining an upward or downward demand at the current moment according to the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment; determining an upward or downward flexibility adjustment capability provided by the distributed power supply at the current moment according to the power of the distributed power supply at the current moment; according to the operation power of the energy storage unit at the current moment, determining the upward or downward flexibility adjustment capability provided by energy storage at the current moment; and according to the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed power supply and the upward or downward flexibility adjustment capability provided by the energy storage, performing optimal configuration on the power system. And the accuracy of optimal configuration of the power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization configuration, and in particular to a load-side flexibility resource optimization configuration method and system based on robust optimization. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Renewable energy generation represented by wind and solar has the significant characteristics of intermittency and volatility, which makes the power system face unprecedented uncertainty challenges. In this case, flexibility becomes a key attribute for the power system to cope with uncertainty and maintain safe and stable operation. Therefore, reasonable planning and allocation of flexibility resources are crucial for the construction and stable operation of new power systems. At present, new flexibility resources such as distributed generation, demand response, and energy storage systems play an increasingly important role in power systems. However, existing research still has many shortcomings in the allocation of flexibility resources. Some research focuses only on meeting the balance of electricity, while ignoring the impact of system flexibility requirements on resource allocation, which makes it difficult for the system to effectively cope with various uncertainties in actual operation and threatens stability. In addition, when describing flexibility requirements, existing methods such as scenario methods and interval methods have certain limitations and cannot accurately describe the flexibility requirements of each period, which in turn affects the rationality and effectiveness of resource allocation. In summary, there is an urgent need for an innovative method to optimize the allocation of load-side flexibility resources to give full play to its supporting role in the operation of the power system in typical scenarios and improve the system's ability to cope with the uncertainty of new energy. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a load-side flexibility resource optimization configuration method and system based on robust optimization, which fully considers the flexibility requirements of the power system in each time period, optimizes the configuration of the power system, and improves the accuracy of the power system optimization configuration.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] First, a load-side flexibility resource optimization configuration method based on robust optimization is proposed, including:

[0007] Obtain the load power, distributed power source power and energy storage unit operating power of the power system;

[0008] According to the load power and distributed generation power at the current moment, determine the upper and lower bounds of the net load deviation at the current moment;

[0009] Determine the upward or downward demand at the current moment based on the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment;

[0010] According to the current distributed power generation power, determine the upward or downward flexibility adjustment capability provided by the distributed power generation at the current moment;

[0011] According to the current operating power of the energy storage unit, determine the upward or downward flexibility adjustment capability provided by the energy storage at the current moment;

[0012] The power system is optimally configured based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by distributed power sources, and the upward or downward flexibility adjustment capability provided by energy storage.

[0013] Furthermore, the upward demand at the current moment is the maximum value of the net load value at the previous moment minus the net load value at the current moment and 0;

[0014] The downward demand at the current moment is the maximum value of the net load lower limit at the current moment and 0, minus the net load value at the previous moment.

[0015] Furthermore, the upward flexibility adjustment capability provided by the distributed power generation is to select the minimum value from the upper bound of the distributed power generation power minus the distributed power generation power and the distributed power generation upward climbing power;

[0016] The downward flexibility regulation capability provided by the distributed power generation is to select the minimum value from the distributed power generation power minus the lower limit of the distributed power generation power and the distributed power generation downward climbing power.

[0017] Furthermore, the upward flexibility adjustment capability provided by the energy storage is to select the minimum value from the maximum charging power of the energy storage unit minus the operating power of the energy storage unit, the upward climbing power of the energy storage unit, and the discharge power of the energy storage unit;

[0018] The downward flexibility adjustment capability provided by energy storage is to select the minimum value from the minimum charging power of the energy storage unit plus the operating power of the energy storage unit, the downward climbing power of the energy storage unit and the charging power of the energy storage unit.

[0019] Furthermore, the optimal configuration result of the power system is determined based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed power sources and the upward or downward flexibility adjustment capability provided by the energy storage, and the supply and demand balance constraints of the power system.

[0020] Furthermore, with the goal of minimizing the planned investment and operating cost of the new flexible resources on the load side, the optimal configuration result of the power system is determined according to the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed generation and the upward or downward flexibility adjustment capability provided by the energy storage, and the supply and demand balance constraints of the power system;

[0021] or, the planned investment and operating costs of the new load-side flexibility resources include the investment costs of the new load-side flexibility resources and the total operating costs of the new load-side flexibility resources;

[0022] or,

[0023] The investment cost of new flexibility resources on the load side includes the investment cost of distributed generation and energy storage units;

[0024] or,

[0025] The total operating cost of new flexible resources on the load side includes the operating cost of distributed generation, energy storage, thermal power units, electricity purchase cost and electricity sales cost;

[0026] Or, distributed power sources include wind power and photovoltaic power generation;

[0027] Energy storage includes electrochemical energy storage and hydrogen energy storage.

[0028] Secondly, a load-side flexibility resource optimization configuration system based on robust optimization is proposed, including:

[0029] A data acquisition unit, used to acquire the load power, distributed power source power and energy storage unit operating power of the power system;

[0030] The net load demand determination unit is used to determine the upper and lower bounds of the net load deviation at the current moment according to the load power and distributed power supply power at the current moment; and to determine the upward or downward demand at the current moment according to the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment;

[0031] A distributed power supply regulation capability determination unit, used to determine the upward or downward flexibility regulation capability provided by the distributed power supply at the current moment according to the distributed power supply power at the current moment;

[0032] The energy storage regulation capacity determination unit is used to determine the upward or downward flexibility regulation capacity provided by the energy storage at the current moment according to the operating power of the energy storage unit at the current moment;

[0033] The power system optimization configuration unit is used to optimize the configuration of the power system according to the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed power sources, and the upward or downward flexibility adjustment capability provided by the energy storage.

[0034] In a third aspect, a computer device is provided, the device comprising:

[0035] a processor adapted to execute a computer program;

[0036] A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the load-side flexibility resource optimization configuration method based on robust optimization proposed in the first aspect is implemented.

[0037] In a fourth aspect, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the load-side flexibility resource optimization configuration method based on robust optimization proposed in the first aspect.

[0038] In a fifth aspect, a computer program product is proposed, which includes a computer program. When the computer program is executed by a processor, it implements the load-side flexibility resource optimization configuration method based on robust optimization proposed in the first aspect.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention proposes a method and system for optimizing the configuration of load-side flexibility resources based on robust optimization. When determining the optimal configuration plan of the power system, the method determines the flexibility demand of the net load in each time period and the flexibility adjustment capability of the distributed power source and energy storage through the load in each time period, the power of the distributed power source and the power of the energy storage unit, thereby achieving accurate optimal configuration of the power system based on the flexibility demand of the net load and the flexibility adjustment capability of the distributed power source and energy storage.

[0041] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0043] Figure 1 A flow chart of a method for optimizing configuration of load-side flexibility resources based on robust optimization disclosed in an embodiment. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0047] Example 1

[0048] In order to accurately determine the optimal configuration scheme of the power system, in this embodiment, a load-side flexibility resource optimization configuration method based on robust optimization is disclosed, which comprehensively considers the characteristics of various new flexibility resources, constructs a robust optimization configuration model, and adopts an effective solution algorithm to achieve scientific configuration of resources, improve the operating performance of the power system in different typical scenarios, and ensure the economy, reliability and flexibility of the system.

[0049] like Figure 1 As shown, the load-side flexibility resource optimization configuration method based on robust optimization disclosed in this embodiment includes:

[0050] Obtain the load power, distributed power source power and energy storage unit operating power of the power system;

[0051] According to the load power and distributed generation power at the current moment, determine the upper and lower bounds of the net load deviation at the current moment;

[0052] Determine the upward or downward demand at the current moment based on the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment;

[0053] According to the current distributed power generation power, determine the upward or downward flexibility adjustment capability provided by the distributed power generation at the current moment;

[0054] According to the current operating power of the energy storage unit, determine the upward or downward flexibility adjustment capability provided by the energy storage at the current moment;

[0055] The power system is optimally configured based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by distributed power sources, and the upward or downward flexibility adjustment capability provided by energy storage.

[0056] The distributed power source of this embodiment includes wind power and photovoltaic power generation; the energy storage includes electrochemical energy storage and hydrogen energy storage.

[0057] According to the current load power and distributed generation power, the process of determining the upper and lower bounds of the net load deviation at the current moment is:

[0058] According to the load power and distributed power supply power at the current moment, determine the upper and lower bounds of the current load deviation and the upper and lower bounds of the distributed power supply deviation;

[0059] Among them, the upper and lower bounds of wind power deviation are:

[0060] The upper and lower bounds of photovoltaic power generation deviation are:

[0061] The upper and lower bounds of the electric load deviation are:

[0062] in, and are the upper and lower limits of the electric load deviation, and is the upper and lower bounds of the photovoltaic power generation deviation, and is the upper and lower limits of wind power deviation, L wt,t , L pv,t , L c,t are wind power, photovoltaic power and load power at time t respectively; wt , pv , c They are wind power fluctuation system, photovoltaic power generation fluctuation coefficient and load fluctuation coefficient, which are determined based on empirical data.

[0063] According to the upper and lower bounds of the current electric load deviation and the upper and lower bounds of the distributed power source deviation, the upper and lower bounds of the current net load deviation are determined. The upper and lower bounds of the net load deviation are:

[0064]

[0065] in, They are the upper bound and lower bound of net load deviation respectively.

[0066] This embodiment also determines the value range of the net load at the current moment, and the value range of the net load is:

[0067]

[0068] Among them, [L t ] is the value range of net load; are the lower and upper deviations of the net load at time t, L t is the net load value at time t, L t =L c,t -L wt,t -L pv,t .

[0069] This embodiment not only determines the upper and lower bounds of the net load deviation at the current moment, but also considers the upward and downward directions of the flexibility demand, and calculates the upward or downward demand at the current moment, wherein the upward demand at the current moment is the maximum value of the net load value at the previous moment minus the upper bound of the net load at the current moment and 0;

[0070] The downward demand at the current moment is the maximum value of the net load lower limit at the current moment and 0, minus the net load value at the previous moment.

[0071]

[0072] in, are the upward demand at time t and the downward demand at time t respectively; L t-1 is the net load value at time t-1.

[0073] By calculating the net load flexibility demand, it can be ensured that when there is a deviation in the net load, the configuration of flexibility resources can still meet the system demand.

[0074] This embodiment also calculates the regulation capabilities of the flexibility resources, including the upward and downward flexibility regulation capabilities provided by the distributed power sources, and the upward and downward flexibility regulation capabilities provided by the energy storage.

[0075] The output power of distributed power sources can regulate the net load. The upward flexibility regulation capability provided by distributed power sources is to select the minimum value from the upper limit of distributed power source power minus the distributed power source power and the distributed power source upward climbing power; the downward flexibility regulation capability provided by distributed power sources is to select the minimum value from the distributed power source power minus the lower limit of distributed power source power and the distributed power source downward climbing power.

[0076]

[0077] in, The upward flexibility regulation capability provided by distributed power source i and the downward flexibility regulation capability provided by distributed power source are respectively; P i,DG,t , P i,DG,max , P i,DG,min are the power of distributed generation i, the upper bound of deviation and the lower bound of deviation of distributed generation i respectively; are the upward climbing speed and downward climbing rate of the distributed power supply, Δt is the time interval, is the climbing power, is the downward climbing power, i includes wind power and photovoltaic power generation.

[0078] The new type of energy storage has a fast charging and discharging rate, and can provide a certain degree of flexible adjustment on both smaller and longer time scales. The adjustment capability mainly depends on the maximum storage capacity and the maximum charging and discharging power.

[0079] The upward flexibility adjustment capability provided by the energy storage is to select the minimum value from the maximum charging power of the energy storage unit minus the operating power of the energy storage unit, the upward climbing power of the energy storage unit, and the discharge power of the energy storage unit;

[0080] The downward flexibility adjustment capability provided by energy storage is to select the minimum value from the minimum charging power of the energy storage unit plus the operating power of the energy storage unit, the downward climbing power of the energy storage unit and the charging power of the energy storage unit.

[0081]

[0082] Among them, P k,t is the operating power of the k-th energy storage unit at time t, with discharge being positive and charge being negative; They are the upward flexibility adjustment capability and downward flexibility adjustment capability provided by the k-th type of energy storage at time t respectively; are the maximum charging and discharging powers of the kth type of energy storage respectively; are the upward climbing rate and downward climbing rate of the k-th energy storage unit respectively; E k,max 、E k,min are the maximum and minimum power that the k-th energy storage unit can store respectively; E k,t is the amount of electricity stored by the k-th energy storage unit at time t; are the charging power and discharging efficiency of the kth type of energy storage unit respectively; is the upward climbing power of the k-th type energy storage unit, is the downward climbing power of the k-th type energy storage unit, is the discharge power of the kth type energy storage unit; is the charging power of the kth type energy storage unit.

[0083] This embodiment determines the optimal configuration result of the power system according to the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed power sources and the upward or downward flexibility adjustment capability provided by the energy storage, and the supply and demand balance constraints of the power system.

[0084] Preferably, with the goal of minimizing the planned investment and operating costs of new flexibility resources on the load side, the optimal configuration result of the power system is determined based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by distributed power sources and the upward or downward flexibility adjustment capability provided by energy storage, and the supply and demand balance constraints of the power system.

[0085] In order to obtain the optimal configuration result of the power system, this embodiment establishes a new load-side flexibility resource optimization configuration model based on a two-stage robust algorithm.

[0086] Two-stage robust optimization aims to solve the problem of parameter uncertainty in the optimization model. By optimizing the worst-case solution, the final solution has a strong ability to resist uncertainty risks. Its goal is to jointly optimize the two-stage decisions while considering the uncertainty of the second-stage parameters. The first-stage decision must be determined first, and the second-stage decision is made after the first-stage decision is determined and the first-stage parameters are clear.

[0087] The specific load-side new flexibility resource optimization configuration model includes an objective function constructed with the goal of minimizing the planned investment and operating costs of new flexibility resources on the load side, and the supply and demand balance constraints of the power system.

[0088] Among them, the planned investment and operating costs of new flexibility resources on the load side include the investment cost of new flexibility resources on the load side and the total operating cost of new flexibility resources on the load side;

[0089] The investment cost of new flexibility resources on the load side includes the investment cost of distributed generation and energy storage units;

[0090] The total operating cost of new flexibility resources on the load side includes the operating cost of distributed power sources, energy storage, thermal power units, electricity purchase costs and electricity sales costs.

[0091] Planning investment and total operating cost of new flexible resources on the load side C sys :C sys =C inv +C op The calculation formula for the planned investment cost is:

[0092]

[0093] in, is the investment cost of distributed power generation, is the unit investment cost of distributed power generation, S DG is the capacity of distributed power generation. is the investment cost of electrochemical energy storage, is the unit investment cost of electrochemical energy storage, S BAT is the electrochemical energy storage capacity. The investment cost of hydrogen energy storage system is is the unit investment cost of hydrogen energy storage system, S HES is the capacity of the hydrogen energy storage system.

[0094] Total operating cost of new flexible resources on the load side C op :

[0095]

[0096] Among them, α DG , α BAT , α HES , α G are the unit distributed power supply, load-side electrochemical energy storage, load-side hydrogen energy storage system, and thermal power unit operation costs, respectively. buy , α sell are the electricity purchase price and electricity sales price, respectively. DG,t , P BAT,t , P HES,t , P G,t are the power of distributed power source, electrochemical energy storage, hydrogen energy storage system and thermal power unit at time t, P buy,t , P sell,t They are the power purchased and sold from the external power grid respectively, and T is 24h.

[0097] The objective function constructed with the goal of minimizing the planning investment and operating cost of new flexible resources on the load side is:

[0098]

[0099] Among them, r is the discount rate, s is the discount period, and the planning cost C inv is the discounted equivalent investment cost.

[0100] In order to analyze the impact of uncertainty factors in the planning process of new flexible resources, the uncertainty set U of load and wind and solar power is established and expressed as:

[0101]

[0102]

[0103] in, are the load power forecast value, wind power forecast value and photovoltaic power forecast value at time t, They are the maximum deviations of load, wind power and photovoltaic power, which are determined based on experience. is a 0-1 variable, Γ is a conservative parameter, which can take different values ​​in planning, and the larger the value, the more conservative it is.

[0104] New load-side flexibility resource optimization configuration model

[0105] Taking into account the flexibility demand and the comprehensive flexibility supply and demand balance constraints, the new flexibility capacity configuration model mentioned above, which aims to minimize the planning investment and operating costs of new flexibility resources on the load side, can be reconstructed into a two-stage robust optimization model.

[0106] For the planning model of the first stage, it is necessary to consider whether the planning results meet the requirements of different operating conditions. The robust optimization model is used to optimize the solution in the worst case, that is, to consider the maximum operating cost under multiple constraints, so that the final solution has a strong ability to resist uncertainty risks. Therefore, the operating model of the second stage is represented by the maximum expected value of the operating cost of each typical day:

[0107]

[0108] Among them, r(y) includes planning capacity constraints of distributed power sources, electrochemical energy storage and hydrogen energy storage systems; h(x,y,u) includes interaction constraints with the external power grid, electrochemical energy storage charging and discharging constraints, hydrogen energy storage system constraints, distributed power constraints, flexible supply and demand balance constraints, etc.; g(x,y,u) includes power generation-load balance constraints, electrochemical energy storage power state constraints, hydrogen energy storage system power state constraints, etc.

[0109] The installed capacity constraint of the flexibility resource representing the installed capacity range of distributed generation, electrochemical energy storage and hydrogen energy storage system is:

[0110]

[0111] Where S DG,max , S DG,min are the upper and lower limits of the distributed generation capacity respectively; S BAT,max , S BAT,min are the upper and lower limits of the electrochemical energy storage configuration capacity; S HES,max , S HES,min They are the upper and lower limits of the configuration capacity for the hydrogen energy storage system.

[0112] The distributed generation constraints are:

[0113]

[0114] In the formula, are the maximum and minimum power of distributed power sources respectively.

[0115] Electrochemical energy storage charging and discharging constraints:

[0116]

[0117] In the formula, E BAT,t 、E max 、E min are the charge of electrochemical energy storage at time t and its upper and lower limits; μ BAT is the charge and discharge efficiency of electrochemical energy storage; X 1 , X 2 It is the charging and discharging state of electrochemical energy storage.

[0118] Hydrogen energy storage system constraints:

[0119]

[0120] In the formula, E is the maximum power of the electrolyzer and the maximum power of the fuel cell respectively; HES,t is the hydrogen energy storage capacity; μ ele , μ FC They are the charging efficiency of the electrolyzer and the discharging efficiency of the fuel cell, respectively.

[0121] The interaction constraints with the external power grid are:

[0122]

[0123] In the formula, are the maximum power purchase and sale respectively; μ 1 , μ 2 are 0-1 variables, representing the state of electricity purchase and sale.

[0124] Generation-load balance constraints:

[0125]

[0126] In the formula, and are the discharge and charging power of the electrochemical energy storage and hydrogen energy storage systems at time t respectively; P load,t is the power of the net load at time t.

[0127] Flexibility supply and demand balance constraints:

[0128]

[0129] In the formula, They are the upward flexibility adjustment capability and downward flexibility adjustment capability provided by energy storage respectively; They are upward demand and downward demand for flexibility; They are the upward flexibility adjustment capability and downward flexibility adjustment capability provided by distributed power source i respectively; Ω new A collection of new types of flexibility resources.

[0130] Through the above two-stage robust optimization model, the planned investment cost, total operating cost and the goal of minimizing the planned investment and total operating cost of flexibility resources are fully considered. At the same time, various resource characteristics and system operation requirements are incorporated into the constraints, which effectively copes with the influence of uncertain factors in the flexibility resource planning process and provides a solid mathematical model foundation for achieving the optimal configuration of flexibility resources on the load side.

[0131] A two-stage hybrid solution algorithm for robust models based on the column and constraint production (C&CG) algorithm and the Kuhn-Tucker (KKT) theorem.

[0132] The optimization configuration model constructed by the present invention is complex. In order to effectively solve the model, a hybrid solution algorithm combining the column and constraint production (C&CG) algorithm and the Kuhn-Tucker (KKT) theorem is adopted. The algorithm decomposes the complex problem into multiple relatively simple sub-problems and gradually approaches the optimal solution by using the iterative relationship between the main problem and the sub-problems. It has high efficiency and accuracy. The following are the detailed solution steps:

[0133] Step 1: Solve the master problem. Use a suitable optimization solver (such as Gurobi, CPLEX, etc.) to solve the master problem whose goal is to minimize the planned investment and operating costs of the new flexible resources on the load side. The solver uses optimization algorithms such as linear programming and integer programming to find the optimal solution based on the objective function and constraints of the master problem. During the solution process, the solver will continuously adjust the values ​​of the decision variables to meet the constraints and minimize the objective function.

[0134] The optimal solution to the main problem is obtained, that is, the capacity configuration scheme of the distributed power supply, electrochemical energy storage, and hydrogen energy storage system and the corresponding objective function value. The optimal solution is used as the optimal cost lower bound currently found for comparison and judgment in subsequent iterations.

[0135] Step 2: Solve the subproblem. The goal of the subproblem is to maximize the minimum value of the second-stage planning investment and operating cost given the first-stage decision variables. Since the subproblem is a two-level optimization problem, the inner-level minimization problem is first transformed into a maximization problem through the Lagrangian duality theory, and the dual variables are introduced to construct the Lagrangian function. Then, combined with the KKT conditions, the inner-level subproblem is transformed into a KKT conditional equation group, and the nonlinear model is transformed into a linearized form to obtain the linearized constraints and variable relationships.

[0136] The optimization solver is used again to solve the transformed linearized subproblem and obtain the optimal solution of the subproblem and the objective function value (upper bound). At the same time, based on the solution results of the subproblem, the new worst scenario μ is determined, that is, the combination of uncertain parameter values ​​corresponding to the maximum operating cost.

[0137] Step 3: Iteration and convergence judgment.

[0138] (1) Update the upper and lower bounds

[0139] The objective function value obtained from the subproblem is used as the new upper bound to update the currently found optimal cost upper bound. Compare the upper bound and the lower bound. If the upper bound - the lower bound ≤ ∈, the algorithm is considered to have converged and the iteration is stopped; otherwise, the next iteration is continued, where ∈ is the preset convergence accuracy, such as 0.01.

[0140] (2) Adding constraints and iterations

[0141] The new worst-case scenario μ * and the corresponding constraints are added to the main problem to form a new main problem. Specifically, adding constraints and * The relevant constraints are added to the main problem to further restrict the feasible domain of the main problem so that the main problem can find a better solution in subsequent iterations.

[0142] (3) Return to step 1, solve the main problem again, and repeat the above iterative process until the convergence condition is met.

[0143] The load-side flexibility resource optimization configuration method based on robust optimization disclosed in this embodiment comprehensively evaluates flexibility requirements by combining the load fluctuation characteristics of different time scales. The flexible resource demand characterization method provides a solid and reliable basis for subsequent resource configuration, ensuring that the configuration scheme can accurately match the actual needs of the power system under different working conditions, and effectively avoiding the problem of resource waste or insufficient configuration caused by inaccurate demand assessment. In the construction of the optimization configuration model, this embodiment comprehensively considers various factors and constructs a complete objective function and constraint condition system. The objective function takes into account the planning investment cost and the system operation cost, and takes the minimization of total cost as the optimization goal. The comprehensive effects of various factors such as traditional power generation, distributed power generation, load adjustment, and charging and discharging of energy storage equipment are considered. Resource capacity constraints reasonably limit the configuration capacity of various types of flexibility resources (such as distributed power sources, electrochemical energy storage, hydrogen energy storage systems, etc.) to avoid excessive or insufficient resource configuration. In the design of the solution algorithm, a hybrid solution algorithm combining advanced column and constraint generation (C&CG) algorithm and KKT theorem is adopted. And the initial uncertainty set and the worst scenario are constructed, laying a solid foundation for subsequent iterative solutions. In the process of solving the main problem, after constructing the objective function and constraints, the optimal solution is found with the help of an efficient optimization solver to obtain the lower bound of the resource allocation scheme. In the sub-problem solving stage, the complex two-layer optimization problem is converted into a linearized form through the Lagrange duality theory and the KKT condition, and then the upper bound and the new worst scenario are solved. In the iterative process, the upper and lower bounds are continuously updated, and whether to continue the iteration is determined according to the convergence judgment condition to ensure that the algorithm finally converges to the optimal solution. This iterative solution method can effectively deal with the uncertainty and complexity in the model and quickly and accurately find the global optimal configuration scheme. In summary, the load-side flexibility resource optimization configuration method proposed in the present invention determines the flexibility demand of the net load in each time period and the flexibility adjustment capability of the distributed power supply and energy storage through the load, distributed power supply power and energy storage unit power in each time period, thereby realizing the accurate optimization configuration of the power system according to the flexibility demand of the net load and the flexibility adjustment capability of the distributed power supply and energy storage, effectively overcoming many deficiencies of the prior art, and significantly improving the configuration efficiency of the flexibility resources in the power system. This method has played an important role in improving the economic efficiency of power system operation (reducing costs) and enhancing system flexibility. It has broad application prospects and important practical application value in the power industry, and provides strong technical support for the intelligent, efficient and sustainable development of power systems.

[0144] Example 2

[0145] In this embodiment, a load-side flexibility resource optimization configuration system based on robust optimization is disclosed, including:

[0146] A data acquisition unit, used to acquire the load power, distributed power source power and energy storage unit operating power of the power system;

[0147] The net load demand determination unit is used to determine the upper and lower bounds of the net load deviation at the current moment according to the load power and distributed power supply power at the current moment; and to determine the upward or downward demand at the current moment according to the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment;

[0148] A distributed power supply regulation capability determination unit, used to determine the upward or downward flexibility regulation capability provided by the distributed power supply at the current moment according to the distributed power supply power at the current moment;

[0149] The energy storage regulation capacity determination unit is used to determine the upward or downward flexibility regulation capacity provided by the energy storage at the current moment according to the operating power of the energy storage unit at the current moment;

[0150] The power system optimization configuration unit is used to optimize the configuration of the power system according to the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed power sources, and the upward or downward flexibility adjustment capability provided by the energy storage.

[0151] The present invention also discloses a computer device, which includes:

[0152] a processor adapted to execute a computer program;

[0153] A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the load-side flexibility resource optimization configuration method based on robust optimization disclosed in Example 1 is implemented.

[0154] The present invention also discloses a computer-readable storage medium, which stores a computer program. The computer program is suitable for being loaded by a processor and executing the load-side flexibility resource optimization configuration method based on robust optimization disclosed in Example 1.

[0155] The present invention also discloses a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the load-side flexibility resource optimization configuration method based on robust optimization disclosed in Example 1.

[0156] The method disclosed in Example 1 can be directly embodied as a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0157] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A load-side flexibility resource optimization configuration method based on robust optimization, characterized in that: include: Obtain the load power, distributed power source power and energy storage unit operating power of the power system; According to the load power and distributed generation power at the current moment, determine the upper and lower bounds of the net load deviation at the current moment; Determine the upward or downward demand at the current moment based on the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment; According to the current distributed power generation power, determine the upward or downward flexibility adjustment capability provided by the distributed power generation at the current moment; According to the current operating power of the energy storage unit, determine the upward or downward flexibility adjustment capability provided by the energy storage at the current moment; The power system is optimally configured based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by distributed power sources, and the upward or downward flexibility adjustment capability provided by energy storage.

2. The load-side flexibility resource optimization configuration method based on robust optimization according to claim 1, characterized in that: The current upward demand is the maximum value of the net load value at the previous moment minus the net load value at the current moment and 0. The downward demand at the current moment is the maximum value of the net load lower limit at the current moment and 0, minus the net load value at the previous moment.

3. The load-side flexibility resource optimization configuration method based on robust optimization according to claim 1, characterized in that: The upward flexibility adjustment capability provided by the distributed power generation is to select the minimum value from the upper limit of the distributed power generation power minus the distributed power generation power and the distributed power generation upward climbing power; The downward flexibility regulation capability provided by the distributed power generation is to select the minimum value from the distributed power generation power minus the lower limit of the distributed power generation power and the distributed power generation downward climbing power.

4. The load-side flexibility resource optimization configuration method based on robust optimization according to claim 1, characterized in that: The upward flexibility adjustment capability provided by the energy storage is to select the minimum value from the maximum charging power of the energy storage unit minus the operating power of the energy storage unit, the upward climbing power of the energy storage unit, and the discharge power of the energy storage unit; The downward flexibility adjustment capability provided by energy storage is to select the minimum value from the minimum charging power of the energy storage unit plus the operating power of the energy storage unit, the downward climbing power of the energy storage unit and the charging power of the energy storage unit.

5. The load-side flexibility resource optimization configuration method based on robust optimization according to claim 1, characterized in that: The optimal configuration result of the power system is determined based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by distributed power sources and the upward or downward flexibility adjustment capability provided by energy storage, and the supply and demand balance constraints of the power system.

6. The load-side flexibility resource optimization configuration method based on robust optimization according to claim 1, characterized in that: With the goal of minimizing the planned investment and operating costs of new flexible resources on the load side, the optimal configuration of the power system is determined based on the upward or downward demand at the current moment, the upward or downward flexibility adjustment capabilities provided by distributed power sources and the upward or downward flexibility adjustment capabilities provided by energy storage, and the supply and demand balance constraints of the power system; or, the planned investment and operating costs of the new load-side flexibility resources include the investment costs of the new load-side flexibility resources and the total operating costs of the new load-side flexibility resources; or, The investment cost of new flexibility resources on the load side includes the investment cost of distributed generation and energy storage units; or, The total operating cost of new flexible resources on the load side includes the operating cost of distributed generation, energy storage, thermal power units, electricity purchase cost and electricity sales cost; Or, distributed power sources include wind power and photovoltaic power generation; Energy storage includes electrochemical energy storage and hydrogen energy storage.

7. Load-side flexibility resource optimization configuration system based on robust optimization, characterized in that: include: A data acquisition unit, used to acquire the load power, distributed power source power and energy storage unit operating power of the power system; The net load demand determination unit is used to determine the upper and lower bounds of the net load deviation at the current moment according to the load power and distributed power supply power at the current moment; and to determine the upward or downward demand at the current moment according to the upper and lower bounds of the net load deviation at the current moment and the net load value at the previous moment; A distributed power supply regulation capability determination unit, used to determine the upward or downward flexibility regulation capability provided by the distributed power supply at the current moment according to the distributed power supply power at the current moment; The energy storage regulation capacity determination unit is used to determine the upward or downward flexibility regulation capacity provided by the energy storage at the current moment according to the operating power of the energy storage unit at the current moment; The power system optimization configuration unit is used to optimize the configuration of the power system according to the upward or downward demand at the current moment, the upward or downward flexibility adjustment capability provided by the distributed power sources, and the upward or downward flexibility adjustment capability provided by the energy storage.

8. An electronic device, characterized in that: The device comprises: a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the method for optimizing and configuring load-side flexibility resources based on robust optimization as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the load-side flexibility resource optimization configuration method based on robust optimization as described in any one of claims 1-6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the load-side flexibility resource optimization configuration method based on robust optimization as described in any one of claims 1 to 6.

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