Dynamically optimized micro-grid multi-target collaborative scheduling method and system

Abstract

The invention relates to the technical field of power grid dispatching, and discloses a dynamically optimized micro-grid multi-target collaborative dispatching method and system, and the method comprises the steps: collecting real-time operation data and historical operation data of a micro-grid, and carrying out the preprocessing of the real-time operation data and the historical operation data, and obtaining standardized data; defining multi-dimensional risk indexes according to the standardized data, and calculating a dynamic weight corresponding to each risk index; establishing a layered optimization architecture according to the dynamic weight, wherein the layered optimization architecture comprises a long-term optimization layer, a short-term correction layer and an equipment control layer; and solving the layered optimization architecture, and outputting an optimal scheduling strategy. According to the scheme, multi-target dynamic balance during micro-grid cooperative scheduling can be realized, and the anti-interference capability of the system for coping with power fluctuation and the scheduling instruction execution precision are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid dispatching, and in particular relates to a dynamically optimized microgrid multi-objective coordinated dispatching method and system. Background Art

[0002] As a key vehicle for distributing distributed energy, microgrid dispatching systems must simultaneously meet multiple objectives. With the increasing penetration of renewable energy and the deepening of power market reform, traditional dispatching methods face significant challenges in adaptability to dynamic environments, multi-objective collaborative optimization, and real-time control accuracy. Developing intelligent dispatching technologies with risk-aware capabilities is crucial for ensuring the reliable and economical operation of microgrids.

[0003] Traditional microgrid scheduling methods suffer from the following major flaws: They employ a weighted summation of multiple objectives using fixed weight coefficients, ignoring the dynamic nature of risk indicators across different time periods. For example, they fail to increase the weight of economic efficiency during periods of significant price fluctuations and fail to promptly strengthen safety constraints during periods of high voltage overshoot risk. Single-layer optimization models struggle to balance long-term economic efficiency with short-term safety, often requiring the introduction of numerous slack variables in the optimization objectives, significantly increasing solution complexity and making it difficult to ensure solution feasibility. The device control and optimization layers operate in an open-loop mode, lacking a rapid adjustment mechanism when actual operating conditions deviate from the optimized plan. This can easily trigger a chain reaction of safety incidents, particularly in situations such as sudden changes in PV output and short-term load surges. Furthermore, existing methods often employ static conversion coefficients for carbon emissions, failing to reflect the impact of real-time grid-side carbon intensity changes on scheduling decisions.

[0004] Therefore, there is an urgent need to develop a dynamically optimized microgrid multi-objective collaborative scheduling method and system that can achieve dynamic balance of multiple objectives during microgrid collaborative scheduling, and improve the system's anti-interference ability to cope with power fluctuations and the execution accuracy of scheduling instructions. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a dynamically optimized microgrid multi-objective collaborative scheduling method and system, which can achieve dynamic balance of multiple objectives during microgrid collaborative scheduling, and improve the system's anti-interference ability to cope with power fluctuations and the execution accuracy of scheduling instructions.

[0006] The present invention provides a dynamically optimized multi-objective coordinated scheduling method for a microgrid, the method comprising the following steps:

[0007] S1. Collect the real-time operation data and historical operation data of the microgrid, pre-process the real-time operation data and historical operation data to obtain standardized data;

[0008] S2. Define multi-dimensional risk indicators based on standardized data and calculate the dynamic weight corresponding to each risk indicator;

[0009] S3. Establish a hierarchical optimization architecture based on dynamic weights. The hierarchical optimization architecture includes a long-term optimization layer, a short-term correction layer, and a device control layer.

[0010] S4. Solve the hierarchical optimization architecture and output the optimal scheduling strategy.

[0011] Furthermore, in S2, the multi-dimensional risk indicators include safety risk indicators, economic risk indicators, and environmental risk indicators;

[0012] Defining multi-dimensional risk indicators based on standardized data includes:

[0013] The calculation formula of the security risk index is as follows:

[0014]

[0015] Among them, R1(t) represents the security risk index value at time t, t represents time t, and N total (t-Δt, t) represents the total number of sampling times in the past Δt time, N vio (t-Δt,t) represents the number of voltage limit violations in the past Δt period;

[0016] The calculation formula of the economic risk index is as follows:

[0017]

[0018] Among them, R2(t) represents the economic risk index value at time t, C grid (t) represents the grid price at time t, μ price represents the historical average electricity price, σ price represents the standard deviation of historical electricity prices;

[0019] The calculation formula for the environmental risk index is as follows:

[0020]

[0021] Among them, R3(t) represents the environmental risk index value at time t, E act (t) represents the comprehensive carbon emission intensity of the microgrid at time t, E target Indicates the carbon emission target value.

[0022] Furthermore, in S2, the formula for calculating the dynamic weight corresponding to each risk indicator is as follows:

[0023]

[0024] Among them, w i (t) represents the dynamic weight of the i-th risk indicator corresponding to time t, β represents the risk sensitivity coefficient, Ri (t) represents the risk index value of the i-th risk index at time t, j represents the j-th risk index, R j (t) represents the risk index value of the j-th risk index at time t.

[0025] Furthermore, in S3, the objective function of the long-term optimization layer is as follows:

[0026]

[0027] Among them, w1(t), w2(t), and w3(t) represent the dynamic weights of the safety risk index at time t, the economic risk index at time t, and the environmental risk index at time t, respectively. safe (t) represents the security cost, C econ (t) represents the economic cost, C env (t) represents environmental protection cost;

[0028] Constraints include:

[0029] SOC(t+1)=SOC(t)+η·P ESS (t)·Δt';

[0030] P gen (t)+P grid (t) = P load (t)+P ESS (t);

[0031] Among them, SOC(t) represents the energy storage charge state at time t, SOC(t+1) represents the energy storage charge state at time t+1, η represents the energy storage charge and discharge efficiency, P ESS (t) represents the energy storage charging and discharging power at time t, Δt' represents the optimization time step, P gen (t) represents the local power generation at time t, P grid (t) represents the main network power purchase at time t, P load (t) represents the total load power at time t.

[0032] Furthermore, the expression of security cost is as follows:

[0033] C safe (t)=λ·|V(t)-V ref |;

[0034] Where λ represents the penalty coefficient, V(t) represents the actual value of the bus voltage at time t, and V ref Indicates rated voltage;

[0035] The economic cost is expressed as follows:

[0036] C econ (t) = c grid (t)·P grid (t)+c diesel ·P diese (t)+c start y(t);

[0037] Among them, c grid (t) represents the main grid electricity price at time t, c diesel P represents the unit output operation and maintenance cost of the diesel engine, diesel (t) represents the diesel engine output at time t, c start represents the single start-stop cost of the diesel engine, y(t) represents the start-stop status of the diesel engine at time t;

[0038] The expression of environmental protection cost is as follows:

[0039] C env (t) = c carbon ·(e diesel ·P diese (t)+e grid ·P grid (t));

[0040] Among them, c carbon represents the unit price of carbon emissions, e diesel Indicates the carbon emission coefficient of diesel engine, e grid Represents the carbon emission factor of main grid electricity.

[0041] Furthermore, in S3, the objective function of the short-term correction layer is as follows:

[0042]

[0043] Where τ represents the time index in the rolling optimization window, w1(τ) and w2(τ) represent the dynamic weight of the safety risk index at the time corresponding to τ and the dynamic weight of the economic risk index at the time corresponding to τ, respectively, ΔV represents the voltage deviation, C adjust (τ) represents the plan adjustment cost at the time corresponding to τ;

[0044] Constraints include:

[0045] |P′ ESS (τ)-P ESS (τ)|≤ΔP max ;

[0046] Among them, P' ESS (τ) represents the corrected energy storage charge and discharge power at the time corresponding to τ, P ESS (τ) represents the energy storage charging and discharging power at the time corresponding to τ issued by the long-term optimization layer, ΔPmax Indicates the maximum step size for a single adjustment of the energy storage power.

[0047] Furthermore, in S3, the device control layer adopts the proportional control rule, and the calculation formula is as follows:

[0048] u(t)=-k p ·(P′ ESS (t)-P actual (t));

[0049] Among them, u(t) represents the power adjustment command output at time t, k p Represents the proportionality coefficient, P' ESS (t) represents the corrected energy storage charging and discharging power at time t, P actual (t) represents the actual output power at time t.

[0050] Furthermore, in S4, the model predictive control algorithm is used to solve the hierarchical optimization architecture and output the optimal scheduling strategy.

[0051] Furthermore, in S1, preprocessing includes outlier removal, missing value filling, and standardization.

[0052] The present invention also provides a dynamically optimized microgrid multi-objective coordinated scheduling system for executing the above-mentioned dynamically optimized microgrid multi-objective coordinated scheduling method. The system includes the following modules:

[0053] The data acquisition module is used to collect the real-time operation data and historical operation data of the microgrid, pre-process the real-time operation data and historical operation data to obtain standardized data;

[0054] Dynamic weight calculation module, used to define multi-dimensional risk indicators based on standardized data and calculate the dynamic weight corresponding to each risk indicator;

[0055] A hierarchical optimization architecture design module is used to establish a hierarchical optimization architecture based on dynamic weights. The hierarchical optimization architecture includes a long-term optimization layer, a short-term correction layer, and a device control layer.

[0056] The target solving module is used to solve the hierarchical optimization architecture and output the optimal scheduling strategy.

[0057] The embodiments of the present invention have the following technical effects:

[0058] The present invention establishes a dynamic weight calculation model based on risk sensitivity coefficients and uses the exponential function normalization method to adjust the weight ratios of safety, economic, and environmental protection indicators in real time, so that the scheduling strategy can adapt to changes in risk preferences under different operating scenarios. It adopts a three-layer collaborative architecture including long-term optimization, short-term correction, and equipment control. Through time decoupling, it handles optimization goals of different time scales separately. The long-term layer is responsible for the preparation of 24-hour economic scheduling plans, the short-term layer performs 6-hour rolling corrections to compensate for prediction errors, and the equipment layer uses proportional control to achieve second-level power tracking, forming a multi-time scale closed-loop optimization mechanism. Combined with the model predictive control algorithm, the rolling optimization problem is re-solved in each control cycle to effectively deal with the uncertainty caused by renewable energy fluctuations and load mutations. This method combines dynamic risk quantification with hierarchical collaborative control. While ensuring the voltage safety level, it can maintain scheduling stability without relying on accurate forecast data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 This is a flow chart of a dynamic optimization microgrid multi-objective coordinated scheduling method provided by an embodiment of the present invention;

[0061] Figure 2 is a schematic diagram of a multi-objective optimization three-dimensional coordinate diagram provided by an embodiment of the present invention;

[0062] Figure 3 It is a structural diagram of a dynamically optimized microgrid multi-objective collaborative scheduling system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0064] The embodiment of the present invention provides a dynamic optimization microgrid multi-objective coordinated scheduling method, Figure 1 This is a flowchart of a dynamic optimization microgrid multi-objective coordinated scheduling method provided by an embodiment of the present invention, see Figure 1, the method comprises the following steps:

[0065] S1. Collect the real-time operation data and historical operation data of the microgrid, pre-process the real-time operation data and historical operation data to obtain standardized data.

[0066] In some embodiments, real-time operating data includes but is not limited to photovoltaic / wind power output, load demand, energy storage SOC, grid electricity price (5-minute granularity), etc.; historical operating data includes but is not limited to historical wind and solar output curves, load peak and valley records, extreme event (typhoon, blizzard) data, etc.

[0067] In some embodiments, preprocessing includes outlier removal, missing value filling, and standardization. Z-score normalization can be used to map the data to a distribution with a mean of 0 and a standard deviation of 1.

[0068] S2. Define multi-dimensional risk indicators based on standardized data, calculate the dynamic weights corresponding to each risk indicator, and quantify the priorities of different goals.

[0069] In some embodiments, the multi-dimensional risk indicators include a safety risk indicator, an economic risk indicator, and an environmental risk indicator; the safety risk indicator is the probability of system voltage exceeding the limit, which reflects the stability of the power grid; the economic risk indicator is the deviation rate between the real-time electricity price and the historical average, which reflects the fluctuation of the power purchase cost; the environmental risk indicator is the difference between the current carbon emission intensity and the microgrid target, which reflects the degree of achievement of the environmental protection target;

[0070] Defining multi-dimensional risk indicators based on standardized data includes:

[0071] The calculation formula of the security risk index is as follows:

[0072]

[0073] Among them, R1(t) represents the security risk index value at time t, t represents time t, and N total (t-Δt, t) represents the total number of sampling times in the past Δt time, N vio (t-Δt,t) represents the number of voltage limit violations in the past Δt period;

[0074] The calculation formula of the economic risk index is as follows:

[0075]

[0076] Among them, R2(t) represents the economic risk index value at time t, C grid (t) represents the grid price at time t, μ price represents the historical average electricity price, σ price represents the standard deviation of historical electricity prices;

[0077] The calculation formula for the environmental risk index is as follows:

[0078]

[0079] Among them, R3(t) represents the environmental risk index value at time t, E act (t) represents the comprehensive carbon emission intensity of the microgrid at time t, E target Indicates the carbon emission target value.

[0080] The safety risk index quantifies the grid operation risk by the ratio of the voltage exceeding the limit frequency to the total number of sampling times. When the voltage exceeds the limit frequently, the index value approaches the maximum value, triggering the weight adjustment mechanism to enhance the safety constraint weight.

[0081] The economic risk index can effectively eliminate the impact of differences in base electricity prices in different regions. When the real-time electricity price deviates significantly from the historical average level, the index value increases, guiding the scheduling strategy to give priority to local power generation or energy storage discharge.

[0082] When the microgrid's carbon emissions exceed the standard due to an increase in the proportion of coal-fired power generation, the risk value of the environmental risk indicator increases positively, prompting the system to increase the proportion of clean energy consumption.

[0083] In some embodiments, the calculation process for all three indicators utilizes a sliding time window mechanism. The window length is adaptively adjusted based on the microgrid's operating mode, and the window is automatically shortened during periods of significant wind and solar resource fluctuations to enhance risk perception. The risk values ​​output by the indicator calculation module are standardized to eliminate dimensional differences, providing a unified benchmark for subsequent dynamic weight allocation.

[0084] In some embodiments, the calculation formula for calculating the dynamic weight corresponding to each risk indicator is as follows:

[0085]

[0086] Among them, w i (t) represents the dynamic weight corresponding to the i-th risk indicator at time t. The weight changes dynamically over time. β represents the risk sensitivity coefficient, which is used to control the response intensity of the weight to the risk. For example, it can be taken as 0.5. R i (t) represents the risk index value of the i-th risk index at time t, j represents the j-th risk index, R j (t) represents the risk index value of the jth risk index at time t. Risk value R i The higher (t), the corresponding weight w i The larger (t) is, the higher the priority should be on reducing this risk during optimization.

[0087] The weight distribution results are passed to the objective function of the optimization model in real time. The objective function of the long-term optimization layer multiplies the weight of each time period by the corresponding dimensional cost item and then adds them up to form a time-varying weighted optimization target. This design automatically increases the economic weight during the daytime period with high electricity prices, focuses on safety indicators during the evening load peak period, and prioritizes environmental protection goals during the nighttime low emission period. The weight update mechanism is triggered synchronously with the rolling optimization window. The dynamic weights of future time periods are recalculated before the start of each optimization cycle to ensure that the scheduling plan always meets the latest risk situation. Through the nonlinear characteristics of the exponential function, the system can achieve a smooth transition of weights when multiple risks occur concurrently, avoiding the optimization target jump problem caused by traditional linear weighting.

[0088] S3. Establish a hierarchical optimization architecture based on dynamic weights. The hierarchical optimization architecture includes a long-term optimization layer, a short-term correction layer, and a device control layer.

[0089] In some embodiments, optimization layers are divided into long, medium, and short timescales. The long-term optimization layer generates a day-ahead dispatch plan on a 24-hour cycle. The short-term correction layer adjusts the energy storage output plan on a rolling basis every six hours. The device control layer tracks power deviations in real time and provides feedback on correction instructions. Ultimately, a model predictive control algorithm is used to iteratively solve the multi-objective optimization problem, outputting a dispatch plan that balances safety constraints, economic costs, and environmental requirements, achieving adaptive and optimized operation of the microgrid throughout the entire time period.

[0090] In some embodiments, the objective function of the long-term optimization layer is as follows:

[0091]

[0092] Among them, w1(t), w2(t), and w3(t) represent the dynamic weights of the safety risk index at time t, the economic risk index at time t, and the environmental risk index at time t, respectively. safe (t) represents the security cost, C econ (t) represents the economic cost, C env (t) represents environmental protection cost;

[0093] Constraints include:

[0094] Energy storage SOC continuity constraint: SOC(t+1)=SOC(t)+η·P ESS (t)·Δt';

[0095] Power balance constraint: P gen (t)+P grid (t) = P load (t)+P ESS (t);

[0096] Among them, SOC(t) represents the energy storage charge state at time t, SOC(t+1) represents the energy storage charge state at time t+1, η represents the energy storage charge and discharge efficiency, P ESS (t) represents the energy storage charging and discharging power at time t, Δt' represents the optimization time step, P gen (t) represents the local power generation at time t, P grid (t) represents the main network power purchase at time t, P load (t) represents the total load power at time t.

[0097] Furthermore, the expression of security cost is as follows:

[0098] C safe (t)=λ·|V(t)-V ref |;

[0099] Where λ represents the penalty coefficient, V(t) represents the actual value of the bus voltage at time t, and V ref Indicates rated voltage;

[0100] The economic cost is expressed as follows:

[0101] C econ (t) = c grid (t)·P grid (t)+c diesel ·P diese (t)+c start y(t);

[0102] Among them, c grid (t) represents the main grid electricity price at time t, c diesel P represents the unit output operation and maintenance cost of the diesel engine, diesel (t) represents the diesel engine output at time t, c start represents the single start-stop cost of the diesel engine, y(t) represents the start-stop status of the diesel engine at time t;

[0103] The expression of environmental protection cost is as follows:

[0104] C env (t) = c carbon ·(e diesel ·P diese (t)+e grid ·P grid (t));

[0105] Among them, c carbon represents the unit price of carbon emissions, e diesel Indicates the carbon emission coefficient of diesel engine, e grid Represents the carbon emission factor of main grid electricity.

[0106] The safety cost term is constructed by measuring the absolute deviation between the actual voltage and the rated value. When the bus voltage deviates from the safe range, a linearly increasing penalty cost is incurred, prompting the optimization process to prioritize maintaining voltage stability. The economic cost term includes the main grid electricity purchase fee, diesel generator operation and maintenance costs, and start-up and shutdown losses. The main grid electricity purchase fee tracks fluctuations in time-of-use electricity prices in real time, and the diesel generator start-up and shutdown state variables are modeled using binary constraints. The environmental cost term converts total carbon emissions based on the grid-side carbon emission factor and the local diesel engine emission coefficient, and combines this with carbon trading market prices to form the environmental cost.

[0107] The long-term optimization layer is used to develop baseline energy storage charging and discharging plans and grid-interactive power plans. A mixed-integer linear programming algorithm is used to solve the problem, treating the diesel engine start and stop states as discrete variables. A branch-and-bound approach is used to search for the optimal solution that satisfies all constraints. The optimization results generate baseline scheduling plans for each future time period, including diesel engine start and stop sequences, energy storage charging and discharging profiles, and grid-interactive power purchase plans. This provides a reference trajectory for subsequent short-term adjustments.

[0108] In some embodiments, the objective function of the short-term correction layer is as follows:

[0109]

[0110] Where τ represents the time index in the rolling optimization window, w1(τ) and w2(τ) represent the dynamic weight of the safety risk index at the time corresponding to τ and the dynamic weight of the economic risk index at the time corresponding to τ, respectively, ΔV represents the voltage deviation, C adjust (τ) represents the plan adjustment cost at the time corresponding to τ;

[0111] Constraints include:

[0112] |P′ ESS (τ)-P ESS (τ)|≤ΔP max ;

[0113] Among them, P' ESS (τ) represents the corrected energy storage charge and discharge power at the time corresponding to τ, P ESS (τ) represents the energy storage charging and discharging power at the time corresponding to τ issued by the long-term optimization layer, ΔP max Indicates the maximum step size for a single adjustment of the energy storage power.

[0114] The short-term correction layer is used to adjust long-term plans based on real-time deviations in wind and solar output. This layer utilizes a rolling time window optimization mechanism, using the current time window as the starting point to make local adjustments to the scheduling plan for the next few hours. After each rolling optimization, only the correction instructions for the current time window are executed, and the subsequent time period plans are used as forecast information for the next round of optimization. This design ensures rapid responsiveness in the real-time control link while gradually eliminating the cumulative effects of forecast errors through the rolling advancement mechanism.

[0115] In some embodiments, the device control layer adopts a proportional control rule, and the calculation formula is as follows:

[0116] u(t)=-k p ·(P′ ESS (t)-P actual (t));

[0117] Among them, u(t) represents the power adjustment command output at time t, k p Represents the proportional coefficient, which is proportional to the dynamic weight w1(t) of the security risk index, P' ESS (t) represents the corrected energy storage charging and discharging power at time t, P actual (t) represents the actual output power at time t.

[0118] The device control layer executes power commands to maintain voltage and frequency stability. After receiving energy storage charge and discharge power commands from the short-term correction layer, the device control layer generates power adjustment signals using a proportional control algorithm. The control cycle is set to the second level. At the beginning of each cycle, the latest power measurement is read, the current deviation is calculated, and a control signal is generated. The power electronics adjust the charge and discharge currents to achieve precise regulation of the charge and discharge power.

[0119] S4. Solve the hierarchical optimization architecture and output the optimal scheduling strategy.

[0120] In some embodiments, a model predictive control algorithm can be used to solve a hierarchical optimization architecture. The optimization algorithm uses a rolling progress mechanism at each level. Long-term optimization results are embedded as boundary conditions in a short-term correction model. These short-term corrections further constrain the feasible domain of the device control layer. Hierarchical optimization instructions are output, including long-term plans, short-term correction instructions, and device control instructions. The optimal scheduling strategy is derived from these hierarchical optimization instructions.

[0121] Specifically, a model predictive control algorithm is adopted, and a linear programming or quadratic programming solver is called to solve the long-term optimization layer with the goal of minimizing the 24-hour total cost. Based on the energy storage charging and discharging power plan, real-time data, and updated dynamic weights output by the long-term optimization layer, a quadratic programming solver is called to solve the short-term correction layer with the goal of correcting the long-term plan deviation, and the corrected energy storage charging and discharging power set value is obtained. The control instructions are executed through the proportional control rules of the equipment control layer to adjust the energy storage charging and discharging power, diesel engine output, etc.

[0122] For example, real-time data (wind and solar power output, load, SOC, voltage, etc.) can be updated every 5 minutes, and the risk indicator values ​​and corresponding dynamic weights can be dynamically calculated based on the latest data, and the optimization update can be promoted in a rolling window. For the long-term layer, the baseline plan is regenerated every 24 hours (if the wind and solar power forecast changes significantly, it will be triggered in advance), and for the short-term layer, the optimization window is rolled every 1 hour (covering the next 6-hour period).

[0123] In some embodiments, when extreme weather occurs and causes photovoltaic forecasts to deviate significantly from actual values, model predictive control can automatically shorten the optimization window length and increase the rolling optimization frequency to improve the robustness of the scheduling plan.

[0124] In some other embodiments, methods such as the bat algorithm may be used to solve the hierarchical optimization architecture.

[0125] Figure 2 is a schematic diagram of a multi-objective optimization three-dimensional coordinate diagram provided by an embodiment of the present invention, see Figure 2 , which is a schematic diagram of the changes in the safety risk index, economic risk index and environmental risk index in a certain simulation optimization process. When the trajectory shifts toward the negative direction of the safety risk index coordinate axis, it corresponds to unsafe conditions such as increased voltage limit probability and increased equipment overload risk. When the trajectory shifts toward the positive direction of the safety risk index coordinate axis, it reflects stable operating conditions such as voltage stability and power balance achieved through scheduling strategy optimization; when the trajectory shifts toward the negative direction of the economic risk index coordinate axis, it indicates that high-priced electricity purchases and increased equipment losses may occur. When the trajectory shifts toward the positive direction of the economic risk index coordinate axis, it indicates that adjustments are made through scheduling strategy optimization, such as selecting low-price electricity purchases and increasing local power generation revenue. When the trajectory shifts toward the negative direction of the environmental risk index coordinate axis, it reflects an increase in the proportion of high-carbon power sources and an expansion of the carbon footprint. When the trajectory shifts toward the positive direction of the environmental risk index coordinate axis, it is manifested as an increase in the penetration rate of renewable energy and a decrease in carbon emission intensity.

[0126] The present invention establishes a dynamic weight calculation model based on risk sensitivity coefficients and uses the exponential function normalization method to adjust the weight ratios of safety, economic, and environmental protection indicators in real time, so that the scheduling strategy can adapt to changes in risk preferences under different operating scenarios. It adopts a three-layer collaborative architecture including long-term optimization, short-term correction, and equipment control. Through time decoupling, it handles optimization goals of different time scales separately. The long-term layer is responsible for the preparation of 24-hour economic scheduling plans, the short-term layer performs 6-hour rolling corrections to compensate for prediction errors, and the equipment layer uses proportional control to achieve second-level power tracking, forming a multi-time scale closed-loop optimization mechanism. Combined with the model predictive control algorithm, the rolling optimization problem is re-solved in each control cycle to effectively deal with the uncertainty caused by renewable energy fluctuations and load mutations. This method combines dynamic risk quantification with hierarchical collaborative control. While ensuring the voltage safety level, it can maintain scheduling stability without relying on accurate forecast data.

[0127] The embodiment of the present invention further provides a dynamically optimized microgrid multi-objective coordinated scheduling system for executing the above-mentioned dynamically optimized microgrid multi-objective coordinated scheduling method. Figure 3 This is a structural diagram of a dynamic optimization microgrid multi-objective coordinated scheduling system provided by an embodiment of the present invention, see Figure 3 , the system includes the following modules:

[0128] The data acquisition module is used to collect the real-time operation data and historical operation data of the microgrid, pre-process the real-time operation data and historical operation data to obtain standardized data;

[0129] Dynamic weight calculation module, used to define multi-dimensional risk indicators based on standardized data and calculate the dynamic weight corresponding to each risk indicator;

[0130] A hierarchical optimization architecture design module is used to establish a hierarchical optimization architecture based on dynamic weights. The hierarchical optimization architecture includes a long-term optimization layer, a short-term correction layer, and a device control layer.

[0131] The target solving module is used to solve the hierarchical optimization architecture and output the optimal scheduling strategy.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic optimization microgrid multi-objective coordinated scheduling method, characterized in that: The method comprises the following steps: S1. Collecting real-time operation data and historical operation data of the microgrid, and preprocessing the real-time operation data and the historical operation data to obtain standardized data; S2. Define multi-dimensional risk indicators based on the standardized data and calculate the dynamic weight corresponding to each risk indicator; S3. Establishing a hierarchical optimization architecture based on the dynamic weights, wherein the hierarchical optimization architecture includes a long-term optimization layer, a short-term correction layer, and a device control layer; S4. Solve the hierarchical optimization architecture and output the optimal scheduling strategy.

2. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 1, characterized in that: In said S2, said multi-dimensional risk indicators include safety risk indicators, economic risk indicators and environmental risk indicators; Defining multi-dimensional risk indicators based on the standardized data includes: The calculation formula of the security risk index is as follows: Among them, R1(t) represents the security risk index value at time t, t represents time t, and N total (t-Δt, t) represents the total number of sampling times in the past Δt time, N vio (t-Δt,t) represents the number of voltage limit violations in the past Δt period; The calculation formula of the economic risk index is as follows: Among them, R2(t) represents the economic risk index value at time t, C grid (t) represents the grid price at time t, μ price represents the historical average electricity price, σ price represents the standard deviation of historical electricity prices; The calculation formula for the environmental risk index is as follows: Among them, R3(t) represents the environmental risk index value at time t, E act (t) represents the comprehensive carbon emission intensity of the microgrid at time t, E target Indicates the carbon emission target value.

3. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 2, characterized in that: In S2, the calculation formula for calculating the dynamic weight corresponding to each risk indicator is as follows: Among them, w i (t) represents the dynamic weight of the i-th risk indicator corresponding to time t, β represents the risk sensitivity coefficient, R i (t) represents the risk index value of the i-th risk index at time t, j represents the j-th risk index, R j (t) represents the risk indicator value of the j-th risk indicator at time t.

4. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 3, characterized in that: In S3, the objective function of the long-term optimization layer is as follows: Among them, w1(t), w2(t), and w3(t) represent the dynamic weights of the safety risk index at time t, the economic risk index at time t, and the environmental risk index at time t, respectively. safe (t) represents the security cost, C econ (t) represents the economic cost, C env (t) represents environmental protection cost; Constraints include: SOC(t+1)=SOC(t)+η·P ESS (t)·Δt'; P gen (t)+P grid (t)=P load (t)+P ESS (t); Among them, SOC(t) represents the energy storage charge state at time t, SOC(t+1) represents the energy storage charge state at time t+1, η represents the energy storage charge and discharge efficiency, P ESS (t) represents the energy storage charging and discharging power at time t, Δt' represents the optimization time step, P gen (t) represents the local power generation at time t, P grid (t) represents the main network power purchase at time t, P load (t) represents the total load power at time t.

5. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 4, characterized in that: The security cost is expressed as follows: C safe (t)=λ·|V(t)-V ref |; Where λ represents the penalty coefficient, V(t) represents the actual value of the bus voltage at time t, and V ref Indicates rated voltage; The economic cost is expressed as follows: C econ (t)=c grid (t)·P grid (t)+c diesel ·P diese (t)+c start ·y(t); Among them, c grid (t) represents the main grid electricity price at time t, c diesel represents the unit output operation and maintenance cost of the diesel engine, P diesel (t) represents the diesel engine output at time t, c start represents the single start-stop cost of the diesel engine, y(t) represents the start-stop status of the diesel engine at time t; The expression of the environmental protection cost is as follows: C env (t)=c carbon ·(e diesel ·P diese (t)+e grid ·P grid (t)); Among them, c carbon represents the unit price of carbon emissions, e diesel Indicates the carbon emission coefficient of diesel engine, e grid Represents the carbon emission factor of main grid electricity.

6. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 3, characterized in that: In S3, the objective function of the short-term correction layer is as follows: Where τ represents the time index in the rolling optimization window, w1(τ) and w2(τ) represent the dynamic weight of the safety risk index at the time corresponding to τ and the dynamic weight of the economic risk index at the time corresponding to τ, respectively, ΔV represents the voltage deviation, C adjust (τ) represents the plan adjustment cost at the time corresponding to τ; Constraints include: |P′ ESS (t)-P ESS (τ)|≤ΔP max ; Among them, P' ESS (τ) represents the corrected energy storage charge and discharge power at the time corresponding to τ, P ESS (τ) represents the energy storage charging and discharging power at the time corresponding to τ issued by the long-term optimization layer, ΔP max Indicates the maximum step size for a single adjustment of the energy storage power.

7. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 3, characterized in that: In S3, the device control layer adopts a proportional control rule, and the calculation formula is as follows: u(t)=-k p ·(P′ ESS (t)-P actual (t)); Among them, u(t) represents the power adjustment command output at time t, k p Represents the proportionality coefficient, P' ESS (t) represents the corrected energy storage charging and discharging power at time t, P actual (t) represents the actual output power at time t.

8. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 1, characterized in that: In S4, a model predictive control algorithm is used to solve the hierarchical optimization architecture and output an optimal scheduling strategy.

9. A dynamic optimization microgrid multi-objective coordinated scheduling method according to claim 1, characterized in that: In S1, the preprocessing includes outlier elimination, missing value filling and standardization.

10. A dynamically optimized microgrid multi-objective coordinated scheduling system, used to execute a dynamically optimized microgrid multi-objective coordinated scheduling method according to any one of claims 1 to 9, characterized in that: The system includes the following modules: A data acquisition module is used to collect real-time operation data and historical operation data of the microgrid, and pre-process the real-time operation data and the historical operation data to obtain standardized data; A dynamic weight calculation module, used to define multi-dimensional risk indicators based on the standardized data and calculate the dynamic weight corresponding to each risk indicator; a hierarchical optimization architecture design module, configured to establish a hierarchical optimization architecture according to the dynamic weights, the hierarchical optimization architecture comprising a long-term optimization layer, a short-term correction layer, and a device control layer; The target solving module is used to solve the hierarchical optimization architecture and output the optimal scheduling strategy.

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