Cooperative operation optimization method and system for source-network-load-storage integrated system in uncertain environment

Abstract

The invention relates to the technical field of source-network load-storage integration, in particular to a source-network load-storage integration system collaborative operation optimization method and system in an uncertain environment, and the method comprises the steps: constructing a chance constraint and an uncertainty optimization model based on a system optimization target according to an optimization model constraint and a load model constraint; and based on the structure of the source-network-load-storage integrated system, a simplified source-network-load-storage opportunity constraint is constructed, so that the problems that in the prior art, a model for collaborative operation optimization of power supply energy storage and load with a simplified network and other factors cannot be realized, so that the load of a factory is unbalanced, and the production delivery is influenced are solved; according to the method, collaborative optimization of source network load storage integration is realized, the model can consider the uncertainty of renewable energy sources, the ACOPF model of the SOCP of the opportunity constraint is simplified, an exact solution can be ensured to be obtained, and the calculation time is greatly shortened.

Description

Technical Field

[0001] This invention relates to the field of integrated power generation, grid, load and storage technology, and specifically to a method and system for optimizing the collaborative operation of integrated power generation, grid, load and storage systems under uncertain environments. Background Technology

[0002] The source-grid-load-storage (SGLS) integrated system combines power generation, transmission, load and energy storage to improve energy efficiency, promote renewable energy consumption and enhance the resilience of the power system. However, the dynamic coordination mechanism among these multiple elements is not yet perfect, and a refined scheduling framework is urgently needed to achieve coordinated interaction. The problems of existing technologies include (1) not considering the use of the optimal power flow model, but directly optimizing the power source, energy storage and load. However, this method cannot achieve coordinated operation optimization of the "grid" and other factors, and cannot coordinate the optimization of factors such as voltage, current and grid loss. This is contrary to the goal of the source-grid-load-storage integrated system, which is "coordinated operation of the four elements of source, grid, load and storage". (2) using the DC optimal power flow model, but the DC optimal power flow model requires the assumption that the voltage is fixed at 1 p.u. and the phase angle difference is extremely small and negligible. It also cannot coordinate the optimization of energy storage. Point voltage and reactive power, while the source-grid-load-storage integrated system is a small system, and still needs to optimize the reactive power and node voltage within the system; (3) Existing technology 3 (source-grid-load-storage collaborative optimization model): uses the AC optimal power flow model, but the AC power flow model is non-convex and nonlinear, which makes its calculation time long and it is very easy to fall into local optima; (4) only considers how much load can be adjusted when the industrial load is suitable, but does not model in detail, such as how much load can be adjusted under what mode; at the same time, it does not carefully consider the production plan constraints of the industrial load. Without this constraint, frequent load reduction of the factory will affect the normal production of the factory, and thus affect its production delivery. In summary, the existing technology has a model that cannot achieve the collaborative operation optimization of power storage and load with simplified grid and other factors, which leads to unbalanced factory load and affects production delivery. Summary of the Invention

[0003] This invention addresses the problems existing in the prior art by providing a method and system for optimizing the coordinated operation of an integrated source-grid-load-storage system under uncertain environments;

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] Optimization methods for the coordinated operation of integrated power generation, grid, load, and storage systems under uncertain environments include:

[0006] Based on the model for minimizing system operating costs, a system optimization objective is constructed. The model for minimizing system operating costs includes a main grid power purchase cost model, a photovoltaic unit power generation cost model, a wind power resource power generation cost model, and an industrial load demand response participation cost model.

[0007] Based on the system optimization objective, a basic power flow optimization model is constructed according to the optimization model constraints. The optimization model constraints include: power flow model constraints, node power balance constraints, energy storage operation constraints, voltage limit constraints, photovoltaic and wind power generation constraints, and the constraint that power cannot be fed back to the main grid from the source, grid, load and storage.

[0008] Based on the system optimization objective, load model constraints are constructed according to the load regulation characteristics. The load regulation characteristics include mode mutual exclusion constraints that limit the maximum continuous running time under power mode adjustment. The load model constraints include demand-side response regulation capability constraints and factory production plan constraints based on the corresponding power mode.

[0009] Based on the system optimization objective, and according to the constraints of the optimization model and the load model, an uncertainty optimization model is constructed by constructing opportunity constraints, and a simplified source-grid-load-storage opportunity constraint is constructed based on the structure of the integrated source-grid-load-storage system.

[0010] Preferably, the step of constructing the system optimization objective based on the model of minimizing system operating costs includes:

[0011] Based on a deterministic mathematical programming model, the non-convex power flow equations are transformed into a minimum system operating cost model compatible with industrial-grade optimization solvers.

[0012] Based on the problem of coordinated operation of the integrated source-grid-load-storage system, and according to the model of minimizing system operating costs, a system optimization objective is constructed to optimize the coordinated operation among the four elements of source-grid-load-storage, wherein the four elements include energy generation, power grid, industrial load and energy storage.

[0013] Preferably, the model for minimizing system operating costs is as follows:

[0014]

[0015] The For time indexes and their sets, the For photovoltaic indexes and their sets, the For wind power indexes and their sets, the For the industrial load index and its set, the The electricity purchase price from the grid, the aforementioned The electricity purchased from the power grid, the For the cost of photovoltaic power generation, the aforementioned For photovoltaic power generation, the aforementioned For the cost of wind power generation, the aforementioned For wind power, the aforementioned To compensate for industrial load participation in demand-side response, the This refers to the electricity generated by industrial loads participating in demand-side response.

[0016] Preferably, the step of constructing a basic power flow optimization model based on the system optimization objective and according to the constraints of the optimization model includes:

[0017] Based on the system optimization objective, power flow model constraints are constructed, wherein the power flow model constraints are based on the physical rules that electricity must follow to flow in the power grid;

[0018] Based on the system optimization objective, node power balance constraints are constructed, wherein the node power balance constraints are based on the principle that the total inflow power to a node equals the total outflow power to the node.

[0019] Based on the system optimization objective, energy storage operation constraints are constructed, including: dynamic state of charge constraints: associating the current energy storage level with the previous state through charging and discharging actions; energy capacity limit constraints: forcing the stored energy to be between the minimum and maximum limits; and power rate limit constraints: limiting the magnitude of instantaneous charging and discharging power.

[0020] Based on the system optimization objective, voltage limiting constraints and interface constraints are constructed. The voltage limiting constraints are used to force the node voltage to be within the limit value; the interface constraints are used to ensure voltage compatibility between the source-grid-load-storage system and the main grid coupling point.

[0021] Based on the system optimization objective, power generation constraints for photovoltaic and wind power are constructed, wherein the power generation constraints for photovoltaic and wind power are used to force the active power generation and reactive power generation of photovoltaic units and wind turbines to be within control limits.

[0022] Based on the system optimization objective, a constraint is constructed to prevent the source-grid-load-storage system from feeding back into the main power grid. This constraint is used to prohibit reverse power flow and restrict the source-grid-load-storage system to purchasing power only from the main grid.

[0023] Preferably, the constraint for constructing the load model based on the system optimization objective and the load regulation characteristics includes:

[0024] Based on the typical industrial load of silicon production facilities, a load regulation capacity model is constructed according to the load regulation characteristics. This model includes a three-tiered load regulation capacity quantification system.

[0025] First-level load regulation capability mode: Adjusts power by ±15%, with a maximum continuous regulation time of at least 1 hour;

[0026] Second load regulation capability mode: Adjusts power by ±10%, with a maximum continuous regulation time of at least 3 hours;

[0027] Third load regulation capability mode: Adjusts power by ±5%, with a maximum continuous regulation time of at least 5 hours;

[0028] The load regulation capability model based on three-level regulation capability quantification is integrated into the optimization framework through discrete mode to generate mixed integer linear constraints;

[0029] Based on mixed integer linear constraints, response regulation capability constraints are constructed by incorporating response timing constraints with load regulation capability modes.

[0030] The response timing constraints are as follows:

[0031] First time limit: Forced to be inactive for at least 1 hour within any 4-hour window, while running continuously for a maximum of 3 hours in the second load regulation capacity mode;

[0032] Second time limit: Forced to be inactive for at least 1 hour within any 2-hour window, and at most continuously for 1 hour in the third load regulation capacity mode;

[0033] Third timing constraint: Ensure that at any given time t, only one load regulation capacity mode is active;

[0034] Based on mixed integer linear constraints, the factory production plan constraints are constructed by using the tolerance threshold limit and daily electricity consumption limit involving the load regulation capacity mode.

[0035] The tolerance threshold limit and daily power consumption limit:

[0036] Based on the tolerance threshold limit, the total daily operating time of the facility was limited in the second-tier load regulation capacity mode and the third-tier load regulation capacity mode, respectively.

[0037] The total load deviation caused by a certain load regulation capacity mode within a day is limited to within ±5% of the facility's planned daily electricity consumption, so as to ensure that the participation of a certain load regulation capacity mode will not significantly disrupt the production plan or affect the delivery commitment.

[0038] Preferably, the response adjustment capability constraint includes:

[0039] First response adjustment capability constraint:

[0040]

[0041] Among them, the To determine whether the industrial load activates the second-tier load regulation capability mode at time t, the following... To determine whether the second-level load regulation capacity mode is activated for industrial load at time t-1, the following... To determine whether the second-tier load regulation capability mode is activated for industrial load at time t-2, the following... Whether the second-level load regulation capability mode is activated for industrial load at time t-3;

[0042] Second response adjustment capability constraint:

[0043]

[0044] Among them, the To determine whether the third-tier load regulation capability mode is activated at time t for the industrial load, the following... Whether the third-level load regulation capability mode is activated for industrial load at time t-1;

[0045] Third response adjustment capability constraint:

[0046]

[0047] Among them, the To determine whether the first-level load regulation capacity mode is activated at time t for the industrial load, the following... To determine whether the industrial load activates the second-tier load regulation capability mode at time t, the following... This determines whether the third-level load regulation capability mode is activated for industrial load at time t.

[0048] Preferably, the factory production plan constraints include:

[0049] Tolerance threshold constraint:

[0050]

[0051] Wherein, β2 is the maximum allowable time for the industrial load to operate in the second-tier load regulation capacity mode, and β3 is the maximum allowable time for the industrial load to operate in the third-tier load regulation capacity mode. To determine whether the industrial load activates the second-tier load regulation capability mode at time t, the following... Whether the third-level load regulation capability mode is activated for industrial load at time t;

[0052] Daily electricity consumption constraints:

[0053]

[0054] Wherein, t∈T is the time index and its set, and the The electricity generated by industrial loads participating in demand-side response, the This is to meet the electricity demand of industrial loads.

[0055] Preferably, the construction of the uncertainty optimization model by constructing opportunity constraints includes:

[0056] Opportunity constraints aim to limit the need to purchase additional energy or backup from the main grid during real-time operation, and to ensure that the probability of supply and demand imbalance is below a preset risk threshold.

[0057] Based on chance constraints, a chance-constrained power flow optimization model is constructed by integrating the sample average approximation method and mixed integer linear constraints.

[0058] Based on the opportunity-constrained power flow optimization model, and taking into account the structural characteristics of the integrated source-grid-load-storage system, an uncertain renewable energy model is generated through model simplification and approximate estimation of line losses.

[0059] Based on the single-node aggregation model, the final uncertainty optimization model is generated by forcing unidirectional power flow to the source-grid-load-storage system.

[0060] The uncertainty optimization model:

[0061]

[0062] Among them, the Let be the amount of electricity purchased from the grid in opportunity-constrained scenario n.

[0063] Preferably, the model simplification and approximate estimation of line losses include:

[0064] Based on the radial topology of the integrated source-grid-load-storage system, the model is simplified to a single-node aggregation model.

[0065] Based on the single-node aggregation model, a renewable energy model under uncertainty is generated by approximating line loss. The approximation of line loss includes omitting internal branch power flow constraints and approximating the line loss of each scenario through the line loss of the main problem.

[0066] The single-node aggregation model:

[0067]

[0068] Among them, the For the index and set of substations connected to the power grid, the For photovoltaic indexes and their sets, the For wind power indexes and their sets, the For an index of energy storage devices and their collection, the For the industrial load index and its set, the... For an index of energy storage devices and their collection, the For the transmission line index and its set, the For the amount of electricity purchased from the grid in opportunity-constrained scenario n, the For the photovoltaic power generation in chance-constrained scenario n, the For the power generation of wind power in chance-constrained scenario n, the... For the power generation of the energy storage device, the To meet the electricity demand of industrial loads, the The electricity generated by industrial loads participating in demand-side response, the The charging power for the energy storage device, the The active power flowing from i to j on the transmission line is... This represents the active power flowing from j to i along the transmission line.

[0069] Meanwhile, this invention also provides a collaborative operation optimization system for an integrated source-grid-load-storage system under uncertain environments, including:

[0070] Based on any of the above-described collaborative operation optimization systems, the collaborative operation optimization system includes a collaborative operation optimization platform, the collaborative operation optimization platform being used for:

[0071] The system optimization objective module is constructed based on the model of minimizing system operating costs. The model of minimizing system operating costs includes the main grid power purchase cost model, the photovoltaic unit power generation cost model, the wind power resource power generation cost model, and the industrial load demand response participation cost model.

[0072] The module for constructing a basic power flow optimization model is as follows: Based on the system optimization objective and according to the optimization model constraints, a basic power flow optimization model is constructed. The optimization model constraints include: power flow model constraints, node power balance constraints, energy storage operation constraints, voltage limit constraints, photovoltaic and wind power generation constraints, and the constraint that power cannot be fed back to the main grid from the source, grid, load, and storage.

[0073] The load model constraint module is constructed based on the system optimization objective and the load regulation characteristics. The load regulation characteristics include mode mutual exclusion constraints that limit the maximum continuous running time under power mode adjustment. The load model constraints include demand-side response regulation capability constraints and factory production plan constraints based on the corresponding power mode.

[0074] The module for constructing an uncertainty optimization model is as follows: Based on the system optimization objective, and according to the constraints of the optimization model and the load model, an uncertainty optimization model is constructed by constructing opportunity constraints.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] This collaborative operation optimization method comprises four main steps. Addressing the shortcomings of existing technical solutions, this invention proposes a collaborative operation optimization method for integrated power generation, grid, load, and storage systems under uncertain environments. This method involves constructing a system optimization objective, a basic power flow optimization model, load model constraints, and an uncertainty optimization model. It achieves collaborative optimization of the integrated power generation, grid, load, and storage system using the Opportunity-Constrained Second-Order Cone Programming (SOCP) optimal power flow model, the AOCPF model. Based on the load characteristics of industrial loads, namely the capacity of silicon production equipment and the production plan of the factory, an industrial load demand-side response model is established. Opportunity constraints are applied to establish an opportunity-constrained SOCP AOCPF model, enabling the model to consider the uncertainties of renewable energy. By leveraging the characteristics of the integrated power generation, grid, load, and storage system, the opportunity-constrained SOCP AOCPF model is simplified, ensuring an exact solution and significantly reducing computation time. Attached Figure Description

[0077] Figure 1 This is a system flowchart of an embodiment of the present invention;

[0078] Figure 2 This is a demand response price diagram according to an embodiment of the present invention;

[0079] Figure 3 These are model comparison diagrams of embodiments of the present invention;

[0080] Figure 4 This is a planned photovoltaic and wind power output diagram according to an embodiment of the present invention;

[0081] Figure 5 This is an active power diagram obtained from the power grid according to an embodiment of the present invention;

[0082] Figure 6 This is an industrial load demand response diagram according to an embodiment of the present invention;

[0083] Figure 7 This is a state-of-charge diagram of the energy storage system according to an embodiment of the present invention; Detailed Implementation

[0084] It is worth noting that, unless otherwise specified, the methods used in this invention are all conventional methods; and the raw materials and equipment used are all conventional commercially available products, and their sources are not specifically limited.

[0085] Example 1:

[0086] The following is combined with Figures 1-7 The working principle of the collaborative operation optimization method shown in the embodiments will be explained.

[0087] like Figure 1As shown, this collaborative operation optimization method comprises four main steps. Addressing the shortcomings of existing technical solutions, this invention proposes a collaborative operation optimization method for an integrated source-grid-load-storage system under uncertain conditions. This method involves constructing a system optimization objective, a basic power flow optimization model, load model constraints, and an uncertainty optimization model. It achieves collaborative optimization of the integrated source-grid-load-storage system using the Opportunity-Constrained Second-Order Cone Programming (SOCP) optimal power flow model, the AOCPF model. Based on the load characteristics of industrial loads, namely the capacity of silicon production equipment and the production plan of the factory, an industrial load demand-side response model is established. Opportunity constraints are applied to establish an opportunity-constrained SOCP AOCPF model, enabling the model to consider the uncertainties of renewable energy. By leveraging the characteristics of the integrated source-grid-load-storage system, the opportunity-constrained SOCP AOCPF model is simplified, ensuring an exact solution is obtained and significantly reducing computation time.

[0088] Its four main steps include:

[0089] Step 1: Define the optimization objective. The objective of the optimization model in this patent is to minimize the system operating cost, including the cost of purchasing electricity from the main grid, the cost of photovoltaic unit power generation, the cost of wind power generation, and the cost of participating in industrial load demand response.

[0090] Step 2: Establish constraints for the basic optimization model, including SOCP ACOPF power flow model constraints, node balance constraints, voltage limit constraints, energy storage operation constraints, photovoltaic and wind power generation constraints, and the constraint that power cannot be fed back to the main grid from the source, grid, load and storage.

[0091] Step 3: Establish industrial load demand-side response constraints, considering silicon production equipment (three adjustment capability modes, ±5%, ±10%, and ±15%), and construct constraints using the Big-M method to constrain the demand-side response adjustment capability of silicon production equipment in the three modes, without excessively affecting the factory production plan.

[0092] Step 4: Construct opportunity constraints. Using the Sample Average Approximation (SAA) method, establish the ACOPF model of SOCP with opportunity constraints. Based on the characteristics of the integrated source-grid-load-storage system, simplify this model to reduce the power flow constraints of non-primary problems. Use the line loss of the primary problem to estimate the line loss of the non-primary problem. The non-primary problem includes node balance constraints, photovoltaic and wind power generation output constraints, and non-grid power supply constraints.

[0093] In summary, a detailed industrial load demand-side response model was established based on the characteristics of industrial loads (especially silicon production plants). The model includes the adjustable load capacity of silicon production equipment under different conditions, and aims to minimize the impact on the plant's production schedule and product delivery. Opportunity constraints are used to account for the uncertainty of renewable energy, and the Sample Average Approximation (SAA) method and Big-M technique are used for modeling, which is then embedded into the ACOPF of SOCP, thus forming an opportunity-constrained SOCP ACOPF model. Based on the characteristics of the integrated source-grid-load-storage system, the opportunity-constrained SOCP ACOPF model is simplified, thereby simplifying the model, greatly accelerating the calculation speed, and ensuring that an exact solution can be obtained.

[0094] This invention proposes an AC Optimal Power Flow (ACOPF) model based on Opportunity-Constrained Second-Order Cone Programming (SOCP), which includes a detailed industrial load demand response (DR) module to achieve coordinated and optimal operation of the SGLS system. The method first constructs a deterministic SOCP-based ACOPF model framework, then incorporates a demand response module reflecting equipment limitations and production characteristics. Next, the uncertainty of renewable energy generation is accounted for by employing an SOCP-based opportunity-constrained ACOPF model. The resulting opportunity-constrained model is simplified to suit the characteristics of the integrated SGLS system. This simplification significantly improves solution efficiency while maintaining convex relaxation accuracy.

[0095] The following is combined with Figures 1-7 The working principle of the collaborative operation optimization method shown in the embodiments will be explained.

[0096] like Figure 1 As shown, the first step of this invention, based on the model of minimizing system operating costs, constructs a system optimization objective, which includes two main steps: transforming the non-convex power flow equations into a model of minimizing system operating costs compatible with industrial-grade optimization solvers; and constructing a system optimization objective to optimize the coordinated operation among the four elements of source, grid, load, and storage.

[0097] This invention uses a deterministic SOCP model to solve the ACOPF problem of an integrated source-grid-load-storage system. By employing SOCP relaxation, the non-convex power flow equations are transformed into a computationally tractable form compatible with industrial-grade optimization solvers.

[0098]

[0099] The optimization model aims to minimize the total operating cost. Its objective function, as shown in the formula above, consists of four components: the cost of purchasing electricity from the main grid, the cost of generating electricity from photovoltaic units, the cost of generating electricity from wind power resources, and the cost of participating in industrial load demand response.

[0100]

[0101] All constraints apply to each time step t∈T. Let the voltage phasor of node i be V. i,t =|V i,t |∠θ i,t The active and reactive power flows of line l between nodes i and j, as defined in the above formula, use three auxiliary variables: c i,j,t =|V i,t ||V j,t |cos(θ i,t -θ j,t (The product of voltage magnitude and the cosine of phase angle difference), e i,j,t =|V i,t ||V j,t |sin(θ i,t -θ j,t (The product of voltage magnitude and the sine of phase angle difference), and u i,t =|V i,t | 2 (The square of the node voltage amplitude).

[0102]

[0103]

[0104] The above constraint variable c i,j,t Applying symmetry conditions to variable e i,j,t Apply the oblique symmetry condition; the original non-convex equality relation (c i,j,t ) 2 +(e i,j,t ) 2 =u i,t u j,t In the above formula, the constraint is reconstructed as a rotated second-order cone (SOC), thus making the optimization problem convex. This relaxation supports the use of convex optimization solvers while maintaining computational feasibility. After solving, u needs to be evaluated. i,t u j,t With (c i,j,t ) 2 +(e i,j,t ) 2 The residual gap between the two is used to verify the quality of the solution; it is worth noting that in the radial network, the convex relaxation is theoretically guaranteed to hold strictly (i.e., the equation holds), thus ensuring no loss of accuracy under standard operating conditions.

[0105] The following is combined with Figures 1-7 The working principle of the collaborative operation optimization method shown in the embodiments will be explained.

[0106] like Figure 1As shown, the second step of this invention, based on the system optimization objective and according to the optimization model constraints, constructs a basic power flow optimization model, comprising six sub-steps: constructing power flow model constraints, constructing node power balance constraints, constructing energy storage operation constraints, constructing voltage limit constraints and interface constraints, constructing photovoltaic and wind power generation constraints, and constructing constraints prohibiting power backflow from the source-grid-load-storage system to the main grid; specifically, among which:

[0107]

[0108] Node power balance is ensured by the constraints described above; where G i ,Kp i ,Kw i ,S i D i B i These represent the sets of generators, photovoltaic units, wind turbines, energy storage systems (ESS), industrial loads, and adjacent branches connected to node i. Parameter α d This represents the ratio of reactive power demand to active power demand for industrial load d.

[0109]

[0110] Voltage regulation is achieved through the above constraints (Forced node voltage within limits) and interface constraints (Ensure voltage compatibility at the coupling point between the source-grid-load-storage system and the main grid)

[0111]

[0112] Energy storage operation is managed by three constraints; constraints Ensure dynamic state of charge, through an efficiency of η c (Charging) and η d The charging and discharging actions (of energy storage) correlate the current energy storage level with the previous state. Constraints Ensure energy capacity is limited, forcing stored energy to remain between minimum and maximum limits. Constraints Ensure power rate limiting, restricting the magnitude of instantaneous charging and discharging power.

[0113]

[0114] The aforementioned renewable energy generation constraints mandate that the active and reactive power generation of photovoltaic units and wind turbines be within limits; policy constraints prohibit reverse power flow and restrict the source-grid-load-storage system to purchasing power only from the main grid.

[0115]

[0116] The proposed ACOPF model based on deterministic SOCP provides a computationally efficient framework for the coordinated operation of renewable energy generation, power grid, industrial load and energy storage in an integrated source-grid-load-storage system, while also providing theoretical guarantees for the radial network topology.

[0117] The following is combined with Figures 1-7 The working principle of the collaborative operation optimization method shown in the embodiments will be explained.

[0118] like Figure 1 As shown, the third step of this invention, based on the system optimization objective and according to the load regulation characteristics, constructs load model constraints, which includes four sub-steps: constructing a load regulation capacity model based on the load regulation characteristics; integrating the discrete model into the optimization framework to generate mixed integer linear constraints; constructing response regulation capacity constraints through response timing constraints involving the load regulation capacity model; and constructing factory production plan constraints through tolerance threshold constraints and daily electricity consumption constraints involving the load regulation capacity model. Specifically, the construction of the load regulation capacity model based on the load regulation characteristics includes three models: a first-tier load regulation capacity model, a second-tier load regulation capacity model, and a third-tier load regulation capacity model. The response timing constraints include three types of constraints: a first-tier time-series constraint, a second-tier time-series constraint, and a third-tier time-series constraint. The tolerance threshold constraint and daily electricity consumption constraint include two steps: limiting the total daily operating time of the facility under the second-tier and third-tier load regulation capacity models based on the tolerance threshold constraint, and limiting the total load deviation caused by a certain load regulation capacity model within a day to within ±5% of the facility's planned daily electricity consumption.

[0119] Industrial loads play a crucial role in integrated power generation, grid, load, and energy storage systems, particularly in facilitating coordinated interaction among power generation, grid, load, and energy storage. Demand-side response of industrial loads is essential for this coordination. Due to differences in equipment characteristics and production-related features, different types of industrial loads exhibit varying demand response capabilities and preferences.

[0120] This invention analyzes a silicon production facility as a typical industrial load and derives its power regulation (DR) model based on its operating characteristics. This facility uses a submerged arc furnace, characterized by a stable and continuous power consumption curve with minimal fluctuations. Its DR capability is quantified as: ±15% power regulation (maximum duration 1 hour), ±10% power regulation (maximum duration 3 hours), and ±5% power regulation (sustainable for a longer period). Three mutually exclusive DR modes are defined, each represented by a binary variable. and This represents the DR patterns that activate ±5%, ±10%, and ±15% adjustments to the load d at time t. The Big-M method is employed to integrate these discrete patterns into the optimization framework through mixed-integer linear constraints.

[0121]

[0122] All constraints apply to each industrial load d∈D and each time step t∈T. The above constraints describe the boundaries of load adjustment under each active DR mode, where... This indicates that DR mode n is activated.

[0123]

[0124] The timing constraints on DR participation are implemented through two key mechanisms. Mode 2 (±10% adjustment) is limited to a maximum of 3 hours of continuous operation by requiring inactivity for at least one hour within any 4-hour window. Mode 3 (±15% adjustment) is restricted to a maximum of 1 hour of continuous operation by forcing inactivity for at least one hour within any 2-hour window. Mode mutual exclusion constraint. Ensure that at any given time t, only one DR mode is active.

[0125]

[0126] In addition to technical constraints, production-related factors must also be considered. For each d∈D, the above constraints limit the total daily operating time of the facility in Mode 2 and Mode 3, respectively, based on tolerance thresholds β2 and β3.

[0127]

[0128] Finally, the above constraints limit the total load deviation caused by DR within a day to within ±5% of the facility's planned daily electricity consumption. This ensures that DR participation does not significantly disrupt production schedules or affect delivery commitments.

[0129] The following is combined with Figures 1-7 The working principle of the collaborative operation optimization method shown in the embodiments will be explained.

[0130] like Figure 1 As shown, the fourth step of this invention, by constructing opportunity constraints, constructs an uncertain optimization model, which includes four sub-steps: constructing opportunity constraints, constructing a power flow optimization model with opportunity constraints, generating an uncertain renewable energy model, and generating the final uncertain optimization model; specifically, wherein:

[0131] The inherent uncertainties in renewable energy generation pose a significant challenge to the reliable operation of power systems. To address these challenges, this study employs a chance-constrained optimization framework to probabilistically manage the volatility of renewable energy generation. This method ensures that the probability of supply-demand imbalance remains below a preset risk threshold ∈, thereby guaranteeing the robust performance of the system under uncertainty.

[0132] This study focuses on integrated power generation, grid, load, and storage systems located at the end of the distribution network. Due to their relatively small scale, over-reliance on reserves and ancillary services would impose a significant operational burden. Furthermore, purchasing energy or ancillary services from the real-time electricity market is typically costly. Therefore, the opportunity constraints established in this study aim to limit the need for additional energy or reserves to be purchased from the main grid during real-time operation. This is expressed in the following formula:

[0133]

[0134] in Let represent the energy required to be obtained from the main network in scenario n at time t, and ∈ be the probability of acceptable constraint violation.

[0135] This constraint is integrated into the proposed SOCP-based ACOPF framework using the Sample Average Approximation (SAA) method and the Big-M technique. The reconstructed constraint is shown in the following formula.

[0136]

[0137] Among the binary variables Indicates whether a constraint is violated in scenario n at time t; the Big-M constant M is... Relaxing the constraints at that time, and Enforce the violation of probability limits in all scenarios;

[0138] However, incorporating a complete SOCP-based ACOPF model into each scenario significantly increases model complexity because the number of variables, parameters, and constraints increases with the number of scenarios. Furthermore, the accuracy of the SOCP formula depends on a radial network topology. Including branch flow constraints for each scenario can introduce interdependencies, altering the mathematical structure of the radial network, potentially leading to non-radial configurations and loss of solution accuracy. This invention simplifies the integrated source-grid-load-storage system (small-scale and radial topology) into a single-node aggregation model. Internal branch flow constraints are omitted, and the line loss for each scenario is approximated using the line loss from the master problem.

[0139]

[0140]

[0141] Among them, bidirectional tidal current items and This is used to approximate active and reactive power losses across all scenarios. These aggregated node power balance constraints apply to all scenarios n∈N and every time step t∈T. Wherein, and These represent the output of renewable energy generation and the reactive power contribution of the main grid under uncertainty, respectively.

[0142]

[0143] Where η pv and η wt These represent the active power conversion efficiencies of the photovoltaic unit and the wind turbine, respectively. and This indicates the available apparent power capacity of the photovoltaic units and wind turbines, relevant to the specific scenario. The above constraints impose scenario-specific operational limits on the active and reactive power output of the photovoltaic units and wind turbines under uncertainty, ensuring technical feasibility. Finally, constraint (3.4-10) enforces unidirectional power flow to the source-grid-load-storage system, prohibiting reverse power injection into the main grid.

[0144]

[0145] The resulting opportunity-constraint framework strikes a balance between computational feasibility and fidelity to physical and operational constraints, enabling scalable, uncertainty-considered optimization for distributed energy systems.

[0146] Test data

[0147] The proposed model was validated on an integrated source-grid-load-storage system with a hybrid renewable energy configuration, such as... Figure 2 As shown, the system includes 120MW of photovoltaic power generation, 425MW of wind power, a 70MW / 140MWh energy storage system, a 30MVar reactive power compensation device, and industrial loads with an average active power demand of 330MW and a reactive power demand of 90MVar. The electricity generated by the photovoltaic power station is collected at a 35kV voltage level, stepped up to 110kV via a main transformer, and then transmitted to a 220kV substation in the industrial load area via a 110kV line. Additionally, wind power is collected at a 35kV voltage level, stepped up to 220kV, and directly fed into the same 220kV substation. The energy storage and reactive power compensation systems are both located within the photovoltaic power station to enhance operational flexibility. The industrial load mainly consists of mineral thermal furnaces, exhibiting a stable and continuous demand curve. The main grid is connected to the system through this 220kV substation to ensure power exchange capacity.

[0148] The proposed optimization model was solved using the CPLEX solver on a machine equipped with a 13th generation... CoreTM The simulation was performed on a standard office laptop with an i7-1360P processor. The simulation parameters used were the photovoltaic (PV) and wind power generation data predicted for the 24 hours prior to March 15, 2023. The modeling of PV and wind power prediction errors was based on the "Two Detailed Rules" issued by the Northwest Regulatory Bureau regarding wind and solar power prediction evaluation standards, requiring wind power prediction errors to be below 25% and PV prediction errors to be below 20%. The grid-purchased electricity price data came from the 2023 industrial and commercial electricity prices of the State Grid Xinjiang Electric Power Company. The costs of PV and wind power generation were set at RMB 165.69 / MWh and RMB 257.05 / MWh, respectively, while the demand response price for industrial load was strategically positioned between the grid-purchased electricity price and the wind power cost, such as... Figure 2 As shown.

[0149] Simulation results

[0150] like Figure 4 As shown, simulation tests were conducted on the proposed deterministic SOCP-based ACOPF model and its chance-constrained variants in an integrated source-grid-load-storage system. For the deterministic SOCP-based ACOPF model, the total computation time was 16 seconds, and the maximum SOC relaxation error (defined as u) was... i,t u j,t -(c i,j,t ) 2 -(e i,j,t ) 2 The value reached 9.79 × 10 -8 This negligible relaxation error confirms that the optimal solution obtained from the proposed deterministic SOCP-based ACOPF simulation is an exact solution.

[0151] The SOCP-based ACOPF chance constraint model was evaluated using 100 scenarios, with a constraint violation probability of ∈=5%. The simplified model customized for the characteristics of the integrated source-grid-load-storage system in this invention had a total computation time of 254 minutes and a maximum SOC relaxation error of 7.23 × 10⁻⁶. -6 Despite the increased complexity, this error magnitude remains small enough to guarantee an exact solution and verify that the obtained optimal solution is physically feasible. For comparative analysis, simulations were also performed on the original (non-simplified) chance-constraint model. The results show a computation time as high as 2046 minutes and a maximum SOC relaxation error as high as 0.513, which is unacceptable. These findings indicate that the original model not only lacks practical feasibility but also produces inaccurate solutions inconsistent with physical power flow constraints, making it unsuitable for power system dispatching and operation.

[0152] like Figure 5The diagram illustrates the optimal scheduling results for planned solar and wind power output under both deterministic and SOCP-based ACOPF chance-constrained model formulations. Wind power output peaks between 05:00 and 12:00 and between 18:00 and 23:00. Solar power generation occurs between 08:00 and 18:00. The deterministic formula schedules higher solar and wind power output throughout the day, particularly during the peak wind power periods of 05:00–12:00 and 18:00–23:00. However, this scheduling ignores prediction errors. In contrast, the chance-constrained formula enforces the aforementioned chance-constrained rule, resulting in a significant reduction in planned wind power output during peak wind power periods. Solar power output, due to its lower total output and less prediction uncertainty, is only moderately reduced between 12:00 and 15:00.

[0153] like Figure 6 The figure shows the amount of electricity purchased from the grid in each time period. Under the deterministic formula, grid purchases drop to zero from 07:00 to 12:00, reflecting high renewable energy output. Although the electricity price is lowest from 05:00 to 08:00 and highest from 08:00 to 12:00, no electricity is purchased from the grid during this period. This result is consistent with... Figure 4 The peak wind power outputs shown are consistent. The opportunity constraint formula takes into account the uncertainty of renewable energy. It only dispatches zero grid purchases during 09:00 and 11:00 (the two most expensive periods). During all other periods, it increases grid purchases and reduces planned renewable energy generation to meet the opportunity constraint (3.4-1). In the afternoon (14:00–17:00), the decline in renewable energy output coincides with the period of lowest grid prices, prompting both models to increase grid purchases. At night (22:00–24:00), wind and solar output are minimal and grid prices are at their peak, and both models obtain similar amounts of electricity from the grid to meet demand.

[0154] like Figure 6The diagram illustrates the optimal scheduling results of the detailed industrial load DR model within the deterministic and SOCP-based ACOPF opportunity-constrained model frameworks. Positive values ​​indicate load reduction, and negative values ​​indicate load increase. Both models produce schedules that adhere to the equipment constraints and production characteristics described in Section 3.3. Under the deterministic framework, industrial load is reduced at night when renewable energy output is zero. The facility increases its load during the period from 7:00 to 11:00 when renewable energy generation peaks and grid purchases are zero. Industrial load is again reduced in the evening (20:00–24:00) when grid prices peak and renewable energy generation is insufficient. The opportunity-constrained framework incorporates the uncertainty of renewable energy generation. It increases industrial load from 5:00 to 8:00 (before renewable energy peaks) to take advantage of low electricity prices. From 8:00 to 12:00 (the period of highest electricity prices), it reduces industrial load. Then, industrial load is increased from 14:00 to 17:00 (during low electricity price periods), and finally, load is reduced from 17:00 to 24:00 (during peak evening electricity price periods).

[0155] like Figure 7 As shown, the optimal charge and discharge scheduling of the energy storage system is depicted under deterministic and SOCP-based ACOPF opportunity-constrained model formulas. Under the deterministic model, charging starts at 4:00 (the period when wind power output begins and grid electricity price is at its lowest (4:00–7:00)) and continues until 8:00 (when renewable energy output reaches its peak). Discharge occurs from 8:00 to 11:00 (the period of highest grid electricity price and abundant renewable energy supply). After 11:00, the energy storage's state of charge drops to zero and remains there until 14:00, at which point a price decrease triggers a second charging period, lasting until 17:00. At night, minimal renewable energy output and peak grid electricity price again trigger discharge. The opportunity-constrained formula initiates charging of the energy storage system at 09:00 and 11:00, reflecting forecast uncertainty and available surplus renewable energy (these two periods have zero grid purchases). At 10:00 (the peak grid electricity price period), the system discharges, extending this discharge period to 11:00–13:00. Charging resumes between 14:00 and 15:00 (the period with the lowest grid electricity price). A final discharge occurs during the night when renewable energy output is minimal and electricity prices are highest. From 13:00 to 24:00, both formulas exhibit similar patterns because lower renewable energy generation makes grid electricity price the primary determinant of energy storage dispatch.

[0156] This invention proposes an ACOPF opportunity-constrained optimization model based on SOCP specifically designed for integrated source-grid-load-storage systems. This model supports coordinated interaction across multiple system components while explicitly capturing renewable energy uncertainties through opportunity constraints. A detailed industrial load DR model considering equipment limitations and production characteristics is embedded in the model. To improve tractability, the formula utilizes simplification measures specific to source-grid-load-storage systems, reducing the number of variables and the overall model size. Therefore, it is easier to obtain an exact solution, and the computation time is significantly reduced.

[0157] Simulation results confirm that the proposed method achieves economic operation and coordinated optimization of the integrated source-grid-load-storage system. Both deterministic and opportunity-constrained SOCP formulas provide high-quality dispatch schemes for renewable energy. The opportunity-constrained version further adjusts the main grid's power purchase decision based on electricity price signals and uncertainty levels. Industrial loads follow optimal DR dispatch under their equipment constraints and production characteristics, and energy storage charging and discharging are also effectively managed. Future work will focus on further accelerating the SOCP-based ACOPF opportunity-constrained model to reduce computation time.

[0158] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for optimizing the coordinated operation of an integrated power generation, grid, load, and storage system under uncertain conditions, characterized in that: include: Based on the model for minimizing system operating costs, a system optimization objective is constructed. The model for minimizing system operating costs includes a main grid power purchase cost model, a photovoltaic unit power generation cost model, a wind power resource power generation cost model, and an industrial load demand response participation cost model. Based on the system optimization objective, a basic power flow optimization model is constructed according to the optimization model constraints. The optimization model constraints include: power flow model constraints, node power balance constraints, energy storage operation constraints, voltage limit constraints, photovoltaic and wind power generation constraints, and the constraint that power cannot be fed back to the main grid from the source, grid, load and storage. Based on the system optimization objective, load model constraints are constructed according to the load regulation characteristics. The load regulation characteristics include mode mutual exclusion constraints that limit the maximum continuous running time under power mode adjustment. The load model constraints include demand-side response regulation capability constraints and factory production plan constraints based on the corresponding power mode. Based on the system optimization objective, and according to the constraints of the optimization model and the load model, an uncertainty optimization model is constructed by constructing opportunity constraints, and a simplified source-grid-load-storage opportunity constraint is constructed based on the structure of the integrated source-grid-load-storage system.

2. The collaborative operation optimization method according to claim 1, characterized in that, The system optimization objective, based on the model for minimizing system operating costs, includes: Based on a deterministic mathematical programming model, the non-convex power flow equations are transformed into a minimum system operating cost model compatible with industrial-grade optimization solvers. Based on the problem of coordinated operation of the integrated source-grid-load-storage system, and according to the model of minimizing system operating costs, a system optimization objective is constructed to optimize the coordinated operation among the four elements of source-grid-load-storage, wherein the four elements include energy generation, power grid, industrial load and energy storage.

3. The collaborative operation optimization method according to claim 2, characterized in that, The model for minimizing system operating costs: The For time indexes and their sets, the For photovoltaic indexes and their sets, the For wind power indexes and their sets, the For the industrial load index and its set, the The electricity purchase price from the grid, the aforementioned The electricity purchased from the power grid, the For the cost of photovoltaic power generation, the aforementioned For photovoltaic power generation, the aforementioned For the cost of wind power generation, the aforementioned For wind power, the aforementioned To compensate for industrial load participation in demand-side response, the This refers to the electricity generated by industrial loads participating in demand-side response.

4. The collaborative operation optimization method according to claim 1, characterized in that, The construction of the basic power flow optimization model based on the system optimization objective and the constraints of the optimization model includes: Based on the system optimization objective, power flow model constraints are constructed, wherein the power flow model constraints are based on the physical rules that electricity must follow to flow in the power grid; Based on the system optimization objective, node power balance constraints are constructed, wherein the node power balance constraints are based on the principle that the total inflow power to a node equals the total outflow power to the node. Based on the system optimization objective, energy storage operation constraints are constructed, including: dynamic state of charge constraints: associating the current energy storage level with the previous state through charging and discharging actions; energy capacity limit constraints: forcing the stored energy to be between the minimum and maximum limits; and power rate limit constraints: limiting the magnitude of instantaneous charging and discharging power. Based on the system optimization objective, voltage limiting constraints and interface constraints are constructed. The voltage limiting constraints are used to force the node voltage to be within the limit value; the interface constraints are used to ensure voltage compatibility between the source-grid-load-storage system and the main grid coupling point. Based on the system optimization objective, power generation constraints for photovoltaic and wind power are constructed, wherein the power generation constraints for photovoltaic and wind power are used to force the active power generation and reactive power generation of photovoltaic units and wind turbines to be within control limits. Based on the system optimization objective, a constraint is constructed to prevent the source-grid-load-storage system from feeding back into the main power grid. This constraint is used to prohibit reverse power flow and restrict the source-grid-load-storage system to purchasing power only from the main grid.

5. The collaborative operation optimization method according to claim 1, characterized in that, The constraints for constructing the load model based on the system optimization objective and load regulation characteristics include: Based on the typical industrial load of silicon production facilities, a load regulation capacity model is constructed according to the load regulation characteristics. This model includes a three-tiered load regulation capacity quantification system. First-level load regulation capability mode: Adjusts power by ±15%, with a maximum continuous regulation time of at least 1 hour; Second load regulation capability mode: Adjusts power by ±10%, with a maximum continuous regulation time of at least 3 hours; Third load regulation capability mode: Adjusts power by ±5%, with a maximum continuous regulation time of at least 5 hours; The load regulation capability model based on three-level regulation capability quantification is integrated into the optimization framework through discrete mode to generate mixed integer linear constraints; Based on mixed integer linear constraints, response regulation capability constraints are constructed by incorporating response timing constraints with load regulation capability modes. The response timing constraints are as follows: First time limit: Forced to be inactive for at least 1 hour within any 4-hour window, while running continuously for a maximum of 3 hours in the second load regulation capacity mode; Second time limit: Forced to be inactive for at least 1 hour within any 2-hour window, and at most continuously for 1 hour in the third load regulation capacity mode; Third timing constraint: Ensure that at any given time t, only one load regulation capacity mode is active; Based on mixed integer linear constraints, the factory production plan constraints are constructed by using the tolerance threshold limit and daily electricity consumption limit involved in the load regulation capacity mode. The tolerance threshold limit and daily power consumption limit: Based on the tolerance threshold limit, the total daily operating time of the facility was limited in the second-tier load regulation capacity mode and the third-tier load regulation capacity mode, respectively. The total load deviation caused by a certain load regulation capacity mode within a day is limited to within ±5% of the facility's planned daily electricity consumption, so as to ensure that the participation of a certain load regulation capacity mode will not significantly disrupt the production plan or affect the delivery commitment.

6. The collaborative operation optimization method according to claim 5, characterized in that, The response adjustment capability constraints include: First response adjustment capability constraint: Among them, the To determine whether the industrial load activates the second-tier load regulation capability mode at time t, the following... To determine whether the second-level load regulation capacity mode is activated for industrial load at time t-1, the following... To determine whether the second-tier load regulation capability mode is activated for industrial load at time t-2, the following... Whether the second-level load regulation capability mode is activated for industrial load at time t-3; Second response adjustment capability constraint: Among them, the To determine whether the third-tier load regulation capability mode is activated at time t for the industrial load, the following... Whether the third-level load regulation capability mode is activated for industrial load at time t-1; Third response adjustment capability constraint: Among them, the To determine whether the first-level load regulation capacity mode is activated at time t for the industrial load, the following... To determine whether the industrial load activates the second-tier load regulation capability mode at time t, the following... This determines whether the third-level load regulation capability mode is activated for industrial load at time t.

7. The collaborative operation optimization method according to claim 5, characterized in that, The factory production plan constraints include: Tolerance threshold constraint: Wherein, β2 is the maximum allowable time for the industrial load to operate in the second-tier load regulation capacity mode, and β3 is the maximum allowable time for the industrial load to operate in the third-tier load regulation capacity mode. To determine whether the industrial load activates the second-tier load regulation capability mode at time t, the following... Whether the third-level load regulation capability mode is activated for industrial load at time t; Daily electricity consumption constraints: Wherein, t∈T is the time index and its set, and the The electricity generated by industrial loads participating in demand-side response, the This is to meet the electricity demand of industrial loads.

8. The collaborative operation optimization method according to claim 1, characterized in that, The construction of an uncertainty optimization model by establishing opportunity constraints includes: Opportunity constraints aim to limit the need to purchase additional energy or backup from the main grid during real-time operation, and to ensure that the probability of supply and demand imbalance is below a preset risk threshold. Based on chance constraints, a chance-constrained power flow optimization model is constructed by integrating the sample average approximation method and mixed integer linear constraints. Based on the opportunity-constrained power flow optimization model, and taking into account the structural characteristics of the integrated source-grid-load-storage system, an uncertain renewable energy model is generated through model simplification and approximate estimation of line losses. Based on the single-node aggregation model, the final uncertainty optimization model is generated by forcing unidirectional power flow to the source-grid-load-storage system. The uncertainty optimization model: Among them, the Let be the amount of electricity purchased from the grid in opportunity-constrained scenario n.

9. The collaborative operation optimization method according to claim 8, characterized in that, The model simplification and approximate estimation of line loss include: Based on the radial topology of the integrated source-grid-load-storage system, the model is simplified to a single-node aggregation model. Based on the single-node aggregation model, a renewable energy model under uncertainty is generated by approximating line loss. The approximation of line loss includes omitting internal branch power flow constraints and approximating the line loss of each scenario through the line loss of the main problem. The single-node aggregation model: Among them, the For the index and set of substations connected to the power grid, the For photovoltaic indexes and their sets, the For wind power indexes and their sets, the For an index of energy storage devices and their collection, the For the industrial load index and its set, the... For an index of energy storage devices and their collection, the For the transmission line index and its set, the For the amount of electricity purchased from the grid in opportunity-constrained scenario n, the For the photovoltaic power generation in chance-constrained scenario n, the For the power generation of wind power in chance-constrained scenario n, the... For the power generation of the energy storage device, the To meet the electricity demand of industrial loads, the The electricity generated by industrial loads participating in demand-side response, the The charging capacity for the energy storage device, the The active power flowing from i to j on the transmission line is... This represents the active power flowing from j to i along the transmission line.

10. A collaborative operation optimization system for an integrated power generation, grid, load, and storage system under uncertain environments, characterized in that: include: Based on the collaborative operation optimization system according to any one of claims 1-9, the collaborative operation optimization system includes a collaborative operation optimization platform, the collaborative operation optimization platform being used for: The system optimization objective module is constructed based on the model of minimizing system operating costs. The model of minimizing system operating costs includes the main grid power purchase cost model, the photovoltaic unit power generation cost model, the wind power resource power generation cost model, and the industrial load demand response participation cost model. The module for constructing a basic power flow optimization model is as follows: Based on the system optimization objective and according to the optimization model constraints, a basic power flow optimization model is constructed. The optimization model constraints include: power flow model constraints, node power balance constraints, energy storage operation constraints, voltage limit constraints, photovoltaic and wind power generation constraints, and the constraint that power cannot be fed back to the main grid from the source, grid, load, and storage. The load model constraint module is constructed based on the system optimization objective and the load regulation characteristics. The load regulation characteristics include mode mutual exclusion constraints that limit the maximum continuous running time under power mode adjustment. The load model constraints include demand-side response regulation capability constraints and factory production plan constraints based on the corresponding power mode. The module for constructing an uncertainty optimization model is as follows: Based on the system optimization objective, and according to the constraints of the optimization model and the load model, an uncertainty optimization model is constructed by constructing opportunity constraints.

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