A method for optimizing the layout of load commutated switches embedded with dynamic regulation evaluation

Abstract

The present application relates to power distribution network planning and economic operation technical field, especially to a kind of load commutating switch optimization layout method embedded dynamic control evaluation, comprising: S1, input the typical day historical load data of distribution area;S2, through optimization layout decision model, iteration is solved using global optimization algorithm, obtains candidate layout scheme;S3, the fitness value of candidate layout scheme is calculated by dynamic control strategy, and feedback to global optimization algorithm in S2;S4, the global optimization algorithm is optimized according to fitness value in the candidate layout scheme, and the optimization result is fed back to the fitness value corresponding to dynamic control strategy calculation in S3;S5, S2~S4 are iterated until convergence, and the candidate layout scheme with the lowest fitness value is output as optimal commutating switch optimization layout.The present application can accurately quantify the real economic benefits of any layout scheme, and find the globally optimal configuration scheme.

Description

Technical Field

[0001] This invention relates to the field of power distribution network planning and economic operation technology, and in particular to a method for optimizing the layout of load switching switches with embedded dynamic control and evaluation. Background Technology

[0002] Using load-switching switches is an effective means of mitigating three-phase imbalance in low-voltage distribution networks. However, the switches themselves incur certain equipment and maintenance costs. In practical engineering, how to selectively install an appropriate number of switches among numerous load branches to achieve the greatest mitigation benefits with minimal investment is a typical optimization problem. In other words, installing too few switches will result in insufficient mitigation effects; installing too many switches will lead to excessively high investment costs, potentially exceeding the electricity savings achieved by reducing network losses, thus resulting in uneconomical investment.

[0003] Existing methods for optimizing the layout of commutator switches typically rely on simplified mathematical models or statistical estimations from historical operation and maintenance data to evaluate the benefits of different layout schemes. For example, some methods may determine the installation location solely based on the size or volatility of the load, while others calculate energy savings by estimating a fixed rate of imbalance reduction. A common drawback of these methods is their failure to accurately quantify the actual governance effects and economic benefits achievable through dynamic adjustment of a specific layout scheme in real-world operation. The lack of precise modeling of the inherent coupling between "layout" and "adjustment" leads to significant discrepancies between the evaluation results and actual conditions, thus affecting the scientific and economic viability of layout decisions.

[0004] Therefore, there is an urgent need for an optimization decision-making method that can accurately assess the real benefits of different layout schemes in order to guide the economical and efficient deployment of commutation switches. Summary of the Invention

[0005] The purpose of this invention is to provide a load switching optimization layout method with embedded dynamic control evaluation, which can accurately quantify the real economic benefits of any layout scheme and find the globally optimal configuration scheme.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for optimizing the layout of load commutator switches with embedded dynamic control evaluation includes:

[0008] S1. Input typical daily historical load data for the distribution radio area;

[0009] S2. By optimizing the layout decision model, a global optimization algorithm is used to iteratively solve the problem and obtain candidate layout schemes;

[0010] S3. Calculate the fitness value of the candidate layout scheme through a dynamic adjustment strategy and feed it back to the global optimization algorithm in S2.

[0011] S4. The global optimization algorithm optimizes the candidate layout schemes according to the fitness value and feeds the optimization result back to the dynamic control strategy in S3 to calculate the corresponding fitness value.

[0012] S5. Iterate through S2 to S4 until convergence, and output the candidate layout scheme with the lowest fitness value as the optimal commutation switch optimization layout.

[0013] Optionally, the decision variable of the optimized layout decision model is the installation judgment vector of the location of the phase-changing switch in N load branches, the objective function is to minimize the annualized total cost taking into account equipment cost and network loss cost, and constraints and penalty terms are introduced.

[0014] Optionally, the annualized total cost is:

[0015] ;

[0016] in, This represents the annualized total cost. The total number of switches to be installed in the candidate layout scheme. For the cost of the i-th switch, This represents the lifetime of the i-th switch. Typical number of days, The total number of sampling points. For the first The precise additional network loss calculated after dynamic adjustment and simulation at each sampling time point. The sampling time interval, This refers to the unit price of electricity.

[0017] Optionally, the global optimization algorithm adopts the chimpanzee optimization algorithm, and the installation judgment vector is encoded as a multi-base vector in the chimpanzee optimization algorithm.

[0018] Optionally, calculating the fitness value of the candidate layout scheme through a dynamic adjustment strategy includes:

[0019] Determine whether the three-phase current imbalance at each sampling time point in the typical daily historical load data exceeds the preset action threshold. If it does, call the dynamic control sub-strategy, and solve for the optimal commutation action that minimizes the three-phase current imbalance at the corresponding sampling time point based on the load of the installed switches determined by the candidate layout scheme. Then, calculate the precise additional network loss at the corresponding sampling time point under the candidate layout scheme based on the three-phase current after the optimal commutation action is executed.

[0020] After traversing all sampling time points, the precise additional network loss of all sampling time points is accumulated, and combined with electricity price, switching equipment cost and lifespan, the annualized total cost corresponding to the candidate layout scheme is calculated as the fitness value.

[0021] Optionally, solving for the optimal commutation action includes:

[0022] When the number of switches to be installed determined by the candidate layout scheme is less than or equal to the preset number threshold, the traversal method is used to calculate all possible commutation combinations to obtain the global optimal commutation action.

[0023] When the number of switches to be installed determined by the candidate layout scheme is greater than the preset number threshold, a heuristic algorithm is used to solve for the approximate optimal commutation action at the corresponding sampling time point.

[0024] Optionally, before calculating the annualized total cost corresponding to the candidate layout scheme, the method further includes checking the maximum three-phase current imbalance after dynamic adjustment simulation at all sampling time points. Does it exceed the preset governance compliance threshold? If the value exceeds the limit, a pre-defined large value penalty term is applied to the fitness value of the candidate layout scheme.

[0025] To further achieve the above objectives, the present invention also provides a load commutator optimization layout system with embedded dynamic control evaluation, comprising:

[0026] The data storage module is used to store typical daily historical load data and preset thresholds for the distribution radio area;

[0027] The layout optimization module is used to optimize the layout decision model and iteratively solve the global optimization algorithm to obtain candidate layout schemes.

[0028] The simulation evaluation module is used to calculate the fitness value of the candidate layout scheme through a dynamic adjustment strategy and feed it back to the layout optimization module. The layout optimization module performs iterative optimization based on the fitness value fed back by the simulation evaluation module until the optimal layout scheme is output.

[0029] The beneficial effects of this invention are as follows:

[0030] (1) High scientific decision-making: By embedding dynamic control simulation, this invention closely integrates the static layout and dynamic operation of the switch, accurately quantifies the cost-benefit relationship, overcomes the blindness of decision-making caused by inaccurate evaluation in traditional methods, and makes the layout decision-making have solid data support.

[0031] (2) Optimal economic benefits: Since the annualized total cost of each candidate solution can be accurately calculated, the present invention can find the global optimal solution that truly balances investment costs and operational benefits, ensuring the maximization of investment returns and avoiding underinvestment or overinvestment.

[0032] (3) Strong applicability: The framework of this invention has good versatility and can easily replace or adjust the internal global optimization algorithm and dynamic control sub-strategy to adapt to power distribution networks of different scales and characteristics. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of a load commutator optimization layout method with embedded dynamic control evaluation according to an embodiment of the present invention.

[0035] Figure 2 This is a flowchart illustrating the calculation of the fitness (i.e., annualized total cost) of a single candidate layout scheme according to an embodiment of the present invention.

[0036] Figure 3 This is a schematic diagram of the topology of the distribution radio station used in an embodiment of the present invention;

[0037] Figure 4 This is a comparison chart showing the effect of mitigating the three-phase current imbalance in a distribution substation over time, according to an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] This embodiment provides a method for optimizing the layout of load commutation switches by embedding dynamic control evaluation, such as... Figure 1 As shown, it includes:

[0041] S1. Input typical daily historical load data for the distribution radio area;

[0042] S2. By optimizing the layout decision model, a global optimization algorithm is used to iteratively solve the problem and obtain candidate layout schemes;

[0043] S3. Calculate the fitness value of the candidate layout scheme through a dynamic adjustment strategy and feed it back to the global optimization algorithm in S2.

[0044] S4. The global optimization algorithm optimizes the candidate layout schemes according to the fitness value and feeds the optimization result back to the dynamic control strategy in S3 to calculate the corresponding fitness value.

[0045] S5. Iterate through S2 to S4 until convergence, and output the candidate layout scheme with the lowest fitness value as the optimal commutation switch optimization layout.

[0046] Specifically, this embodiment aims to solve the problem of determining which branches in a distribution substation with N load branches should be equipped with load switching switches to achieve optimal technical and economic benefits. The specific details include the following:

[0047] 1. Establish an optimal layout decision model;

[0048] The decision variables of the optimized layout decision model are the installation judgment vectors of the positions of the phase switching switches in N load branches. The objective function is to minimize the annualized total cost taking into account equipment cost and network loss cost, and constraints and penalty terms are introduced.

[0049] 1.1 Decision variables;

[0050] The decision variable of the model is Installation judgment vector The definition is as follows:

[0051] ;

[0052] Where N is the total number of load branches in the transformer area, and the vector is the first... element The value can be:

[0053] ;

[0054] The optimization process of a global optimization algorithm is to find the optimal solution. The process of vectorization.

[0055] 1.2 Objective Function;

[0056] The objective function aims to minimize the annualized total cost. This cost consists of two parts: equipment investment cost and network loss cost. Its expression is:

[0057] ;

[0058] a) Annualized equipment cost :

[0059] This cost is the annual cost for all installed load changers. The calculation formula is:

[0060] ;

[0061] in, for The number of elements with a value of 1 in the vector represents the total number of switches installed. The cost of purchasing and installing the i-th switch; Let be the design lifespan (in years) of the i-th switch.

[0062] b) Annualized additional network loss cost :

[0063] This cost represents the electricity bill incurred annually due to additional network losses caused by three-phase imbalance after the installation of switches and dynamic control. Its calculation relies on simulations of historical data, and the formula is as follows:

[0064] ;

[0065] in, The selected typical number of days; This represents the total number of sampling points within a typical day. For the first The precise additional network loss (unit: watts) calculated after dynamic control simulation at each sampling time point. Sampling time interval (unit: hours); Electricity price per unit (unit: yuan / kWh). Additional network losses. Due to neutral line loss Additional line losses and transformer additional losses It consists of three parts.

[0066] To more intuitively illustrate the benefits, the objective function can also be expressed as maximizing the annualized net profit, i.e., minimizing its negative value. Annualized net profit = Annualized electricity cost savings - Annualized equipment cost. The annualized electricity cost savings are:

[0067] ;

[0068] in, In the first Preset additional network loss (in watts) at each sampling time point.

[0069] Therefore, the minimum annualized total cost is:

[0070] .

[0071] 1.3 Constraints and Penalties;

[0072] To ensure the final layout scheme is technically feasible, i.e., the governance effect meets the target, the model introduces constraints based on governance effect and uses penalty terms. This is reflected in the objective function:

[0073] ;

[0074] in, It is the maximum three-phase current imbalance that occurs after dynamic adjustment among all historical data sampling points; It is a preset treatment threshold (such as 15% as required by national standards or 10% more stringent). It is a sufficiently large constant. When a candidate layout scheme fails to keep the imbalance below the threshold at all times, its fitness will be penalized by a huge value, thus being naturally eliminated during the optimization process.

[0075] Finally, the complete fitness function is: .

[0076] 2. Embedded optimization solution process;

[0077] Reference Figure 1 and Figure 2 In this embodiment, the Chimpanzee Optimization Algorithm (ChOA) is used to solve the above model. Its core "layout-control-evaluation" closed-loop process is as follows:

[0078] Step 1: Initialization. The ChOA algorithm randomly generates a population, with each individual (chimpanzee) representing a candidate installation decision vector. .

[0079] Step Two: Precise Evaluation and Fitness Calculation. For each candidate placement scheme in the population... Perform the following evaluation process to calculate its fitness value. :

[0080] a. Calculate equipment costs. According to Calculate the annualized equipment cost by counting the number of 1s. .

[0081] b. Simulate and evaluate the cost of network losses. Initialize the annualized additional network loss cost. Then, the program iterates through the historical load matrix. All m sampling time points. At the... One sampling point:

[0082] i. Determine the imbalance at the current moment. Does it exceed the governance threshold? If the time limit is not exceeded, then no adjustment is considered necessary at this moment. Equal to the loss before regulation Then proceed to the next sampling point.

[0083] ii. If the threshold is exceeded, regulation is necessary. In this case, the system invokes a dynamic regulation sub-strategy. The input to this sub-strategy is the current load current, and... The defined set of loads that can participate in regulation. The goal of the sub-strategy is to find an optimal commutation scheme that makes... Minimum. To balance efficiency and accuracy, this sub-strategy can be set as follows: when the number of controllable loads is small (e.g., less than 10), a traversal method is used to ensure the optimal solution is found; when the number is large, an efficient heuristic algorithm (e.g., standard GA) is used to solve the problem quickly.

[0084] iii. After obtaining the optimal commutation scheme, calculate the commutation result under this scheme. .

[0085] c. Calculate the total cost. After traversing all m sampling points, calculate the total annualized additional network loss cost according to the formula. At the same time, check all sampling points, Does it exceed To determine the penalty item The fitness value of the candidate solution is [value missing]. Ultimately, the fitness value of this candidate solution is [value missing]. .

[0086] Step 3: Iterative Optimization. The fitness values ​​of all candidate solutions are fed back to the ChOA algorithm. Based on the fitness of all candidate solutions in the population, ChOA updates the positions of its leaders (attackers, chasers, etc.) and guides the entire population towards a better solution space (i.e., a lower-cost layout). Steps 2 and 3 are repeated until the preset number of iterations is reached.

[0087] Step 4: Output the results. When the algorithm terminates, output the installation decision vector corresponding to the individual with the lowest fitness in the population. This is the optimal layout scheme we are looking for.

[0088] Experimental verification:

[0089] With a distribution area containing 24 load branches (e.g.) Figure 3 Taking (as shown) as an example, its typical daily average three-phase imbalance is 30.5%. A treatment threshold is set. .

[0090] Using this method for layout optimization, the ChOA algorithm, through iterative optimization, shows that the annualized total cost (represented by a negative number) eventually converges to approximately -17,200 yuan. The output optimal installation judgment vector... In the diagram, the values ​​are 1 for four load branches (6, 9, 16, and 22), and 0 for the rest. This indicates that the optimal layout is to install commutator switches only on these four critical loads.

[0091] The annualized equipment cost of the optimal solution is calculated. for Yuan. Calculations using an embedded evaluation process show that implementing this solution will result in annualized electricity cost savings of approximately 22,575 yuan. Therefore, the annualized net profit is... Yuan. (Refer to...) Figure 4 After adopting this optimal layout scheme and implementing dynamic control, the average three-phase current imbalance of the distribution area was significantly reduced from the original 30.5% to 4.83%, and the maximum imbalance at all time points was controlled at 12.47%, meeting the 15% control requirement. This result verifies that this method can maximize investment benefits while ensuring the control effect.

[0092] To further optimize the technical solution, this embodiment also provides a load commutator optimization layout system with embedded dynamic control and evaluation, including:

[0093] The data storage module is used to store typical daily historical load data and preset thresholds for the distribution radio area;

[0094] The layout optimization module is used to optimize the layout decision model and iteratively solve the global optimization algorithm to obtain candidate layout schemes.

[0095] The simulation evaluation module is used to calculate the fitness value of the candidate layout scheme through a dynamic adjustment strategy and feed it back to the layout optimization module. The layout optimization module performs iterative optimization based on the fitness value fed back by the simulation evaluation module until the optimal layout scheme is output.

[0096] The simulation evaluation module integrates a dynamic control sub-strategy module, which adaptively selects either the traversal method or a heuristic algorithm to solve the problem based on the number of controllable input loads.

[0097] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for optimizing the layout of load commutator switches with embedded dynamic control evaluation, characterized in that, include: S1. Input typical daily historical load data for the distribution radio area; S2. By optimizing the layout decision model, a global optimization algorithm is used to iteratively solve the problem and obtain candidate layout schemes; The decision variable of the optimized layout decision model is the installation judgment vector of the position of the phase commutator in N load branches. The objective function is to minimize the annualized total cost taking into account equipment cost and network loss cost, and constraints and penalty terms are introduced. The annualized total cost is: ； in, This represents the annualized total cost. The total number of switches to be installed in the candidate layout scheme. For the first i Cost of a single switch For the first i One switch lifespan; Typical number of days, The total number of sampling points. For the first The precise additional network loss calculated after dynamic adjustment and simulation at each sampling time point. The sampling time interval, This refers to the unit price of electricity. Before calculating the annualized total cost corresponding to the candidate layout scheme, the method also includes checking the maximum three-phase current imbalance after dynamic adjustment simulation at all sampling time points. Does it exceed the preset governance compliance threshold? If the value exceeds the limit, a pre-defined large value penalty term is applied to the fitness value of the candidate layout scheme. S3. Calculate the fitness value of the candidate layout scheme through a dynamic adjustment strategy and feed it back to the global optimization algorithm in S2. The fitness value of the candidate layout scheme is calculated using a dynamic adjustment strategy, including: Determine whether the three-phase current imbalance at each sampling time point in the typical daily historical load data exceeds the preset action threshold. If it does, call the dynamic control sub-strategy, and solve for the optimal commutation action that minimizes the three-phase current imbalance at the corresponding sampling time point based on the load of the installed switches determined by the candidate layout scheme. Then, calculate the precise additional network loss at the corresponding sampling time point under the candidate layout scheme based on the three-phase current after the optimal commutation action is executed. After traversing all sampling time points, the precise additional network loss of all sampling time points is accumulated, and combined with electricity price, switching equipment cost and lifespan, the annualized total cost corresponding to the candidate layout scheme is calculated as the fitness value; The process of using a dynamic control sub-strategy to solve for the optimal commutation action includes: When the number of switches to be installed determined by the candidate layout scheme is less than or equal to the preset number threshold, the traversal method is used to calculate all possible commutation combinations to obtain the global optimal commutation action. When the number of switches to be installed determined by the candidate layout scheme is greater than a preset threshold, a heuristic algorithm is used to solve for the approximate optimal commutation action at the corresponding sampling time point; S4. The global optimization algorithm optimizes the candidate layout schemes according to the fitness value and feeds the optimization result back to the dynamic control strategy in S3 to calculate the corresponding fitness value. S5. Iterate through S2 to S4 until convergence, and output the candidate layout scheme with the lowest fitness value as the optimal commutation switch optimization layout.

2. The load commutator optimization layout method with embedded dynamic control evaluation according to claim 1, characterized in that, The global optimization algorithm adopts the chimpanzee optimization algorithm, and the installation judgment vector is encoded as a multi-base vector in the chimpanzee optimization algorithm.

3. A load commutator optimization layout system with embedded dynamic control evaluation, the system being used to implement the load commutator optimization layout method with embedded dynamic control evaluation as described in any one of claims 1-2, characterized in that, include: The data storage module is used to store typical daily historical load data and preset thresholds for the distribution radio area; The layout optimization module is used to optimize the layout decision model and iteratively solve the global optimization algorithm to obtain candidate layout schemes. The simulation evaluation module is used to calculate the fitness value of the candidate layout scheme through a dynamic adjustment strategy and feed it back to the layout optimization module. The layout optimization module performs iterative optimization based on the fitness value fed back by the simulation evaluation module until the optimal layout scheme is output.

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