An optimization method for real-time load gap allocation based on particle swarm optimization

Abstract

This invention provides a real-time load gap allocation optimization method based on a particle swarm optimization algorithm, belonging to the field of power system operation and control technology. The optimization method specifically includes the following steps: Step 1: Constructing a fitness function that integrates economic impact and environmental benefits; Step 2: Setting constraints including total load gap constraints and a single enterprise's safe load range; Step 3: Using a particle swarm optimization algorithm to solve the fitness function minimum value and output the optimal load allocation solution. This invention scientifically allocates load gaps through quantitative indicators, reducing communication costs and improving the efficiency, fairness, and environmental benefits of load management. It is used for enterprise flexible load management during peak power demand periods to achieve scientific allocation and optimization of load gaps.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control technology, and in particular to a real-time load gap allocation optimization method based on a particle swarm algorithm, which is used for enterprise elastic load management during peak power demand periods to achieve scientific allocation and optimization of load gaps. Background Art

[0002] Balancing supply and demand during peak summer demand has long been a core challenge in power system operations. To prioritize residential electricity use, implementing flexible load management for industrial enterprises (i.e., adjusting their load to reduce peak demand and fill valleys) has become a key approach.

[0003] However, the current technical system has significant flaws in scientifically allocating peak-shaving responsibilities and utilization ratios across enterprises. Specifically, the core goal of power elastic load management is to achieve load regulation while ensuring grid security, while minimizing socioeconomic and environmental costs. This requires that management solutions simultaneously meet the following conditions: First, quantitative assessment of enterprise supply levels (i.e., load adjustability and importance) to avoid a one-size-fits-all approach; second, real-time response to changes in grid load gaps to rapidly generate allocation plans that balance economic impacts (such as enterprise production losses) with environmental benefits (such as changes in carbon emissions), achieving comprehensive optimization.

[0004] Existing technologies lack a standardized evaluation system, making it impossible to scientifically define the peak-shaving responsibilities that enterprises should bear and the electricity consumption bottom line that needs to be guaranteed through quantitative indicators, resulting in highly subjective management plans; plans are formulated only from a single dimension (such as economic losses or policy compliance), ignoring comprehensive factors such as environmental benefits and production continuity. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization method for real-time load gap allocation based on particle swarm optimization, which scientifically allocates load gaps through quantitative indicators, reduces communication costs, and improves the efficiency, fairness and environmental benefits of load management.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A real-time load gap allocation optimization method based on particle swarm optimization algorithm specifically includes the following steps:

[0008] Step 1: Construct a fitness function that integrates economic impact and environmental benefits;

[0009] Step 2: Set constraints including total load gap constraint and single enterprise safe load range;

[0010] Step 3: Use the particle swarm optimization algorithm to solve the minimum value of the fitness function and output the optimal load distribution plan;

[0011] Furthermore, in step 1, the fitness function is as follows:

[0012] ;

[0013] in, is the load reduction vector of n enterprises;

[0014] is the economic loss point, and the calculation formula is:

[0015] ,in is the GDP per kWh of enterprise i, In response to the average GDP per kWh of enterprises;

[0016] is the environmental loss point, and the calculation formula is: , is the carbon emissions per kWh of enterprise i, In response to the average carbon emissions per kilowatt-hour of electricity produced by enterprises;

[0017] a, b are weight coefficients and a+b=1.

[0018] Furthermore, in step 2, the constraints are set including the total load gap constraint and the single enterprise load constraint;

[0019] Total load notch constraint: ;

[0020] Single enterprise load constraints: ,in is the dynamic baseline load of enterprise i, The security load of enterprise i.

[0021] Further, The calculation method is:

[0022] If it is a working day, take the average load value of the same time in the last five working days;

[0023] If it is a holiday, take the average load value of the same time in the last five holidays.

[0024] Further, The calculation method is:

[0025] ,

[0026] in: Report basic values ​​for enterprises, is the ambient temperature compensation coefficient, is the real-time ambient temperature, is the reference temperature, is the capacity utilization elasticity coefficient, is the real-time capacity utilization.

[0027] Furthermore, the fitness function in step 1 further includes a production continuity penalty term and a response credibility reward term:

[0028]

[0029] ; is the production continuity sensitivity coefficient, is the historical response credibility, 、 is the weight.

[0030] Furthermore, the production continuity sensitivity coefficient Assign values ​​by industry type:

[0031] Continuous production enterprises of hazardous chemicals, ;

[0032] Precision manufacturing / pharmaceutical sterile enterprises, ;

[0033] Other companies, ; is the order urgency parameter;

[0034] The response credibility , is the actual historical pressure drop, This is the historical committed pressure reduction amount.

[0035] Furthermore, the parameters of the particle swarm algorithm in step 1 are set as:

[0036] The number of particles N = 200, the dimension D = n, n is the number of enterprises;

[0037] Maximum number of iterations , learning factor ;

[0038] Inertia Weight according to Dynamic attenuation, is the current iteration number.

[0039] Furthermore, step 3 also includes:

[0040] When particles are initialized, the production continuity sensitivity coefficient is used Assign initial positions in ascending order, The lower the value, the greater the initial load reduction. When the particle swarm diversity is lower than the threshold, the learning factor is automatically adjusted to .

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a quantifiable loss index through a fitness function, scientifically measures the economic and environmental impact of load drop, takes into account both economic losses and environmental benefits, avoids the defects of a single dimension, is used in a large number of enterprise scenarios, quickly generates allocation formulas, and uses GDP per kilowatt-hour and carbon emission data to assist in evaluating the production efficiency and environmental protection level of enterprises, supporting long-term management optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0045] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive. Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] Example 1: See Figure 1 This embodiment discloses a real-time load gap allocation optimization method based on a particle swarm optimization algorithm, which specifically includes the following steps:

[0047] Step 1: Construct a fitness function that integrates economic impact and environmental benefits;

[0048] Step 2: Set constraints including total load gap constraint and single enterprise safe load range;

[0049] Step 3: Use the particle swarm optimization algorithm to solve the minimum value of the fitness function and output the optimal load distribution plan;

[0050] Furthermore, in step 1, the fitness function is as follows:

[0051] ;

[0052] in, is the load reduction vector of n enterprises;

[0053] is the economic loss point, and the calculation formula is:

[0054] ,in is the GDP per kWh of enterprise i, In response to the average GDP per kWh of enterprises;

[0055] is the environmental loss point, and the calculation formula is: , is the carbon emissions per kWh of enterprise i, In response to the average carbon emissions per kilowatt-hour of electricity produced by enterprises;

[0056] a, b are weight coefficients and a+b=1.

[0057] Furthermore, in step 2, the constraints are set including the total load gap constraint and the single enterprise load constraint;

[0058] Total load notch constraint: ;

[0059] Single enterprise load constraints: ,in is the dynamic baseline load of enterprise i, The security load of enterprise i.

[0060] Further, The calculation method is:

[0061] If it is a working day, take the average load value of the same time in the last five working days;

[0062] If it is a holiday, take the average load value of the same time in the last five holidays.

[0063] Further, The calculation method is:

[0064] ,

[0065] in: Report basic values ​​for enterprises, is the ambient temperature compensation coefficient, is the real-time ambient temperature, is the reference temperature, is the capacity utilization elasticity coefficient, is the real-time capacity utilization.

[0066] Furthermore, the fitness function in step 1 further includes a production continuity penalty term and a response credibility reward term:

[0067]

[0068] ; is the production continuity sensitivity coefficient, is the historical response credibility, 、 is the weight.

[0069] Furthermore, the production continuity sensitivity coefficient Assign values ​​by industry type:

[0070] Continuous production enterprises of hazardous chemicals, ;

[0071] Precision manufacturing / pharmaceutical sterile enterprises, ;

[0072] Other companies, ; is the order urgency parameter;

[0073] The response credibility , is the actual historical pressure drop, This is the historical committed pressure reduction amount.

[0074] Furthermore, the parameters of the particle swarm algorithm in step 1 are set as:

[0075] The number of particles N = 200, the dimension D = n, n is the number of enterprises;

[0076] Maximum number of iterations , learning factor ;

[0077] Inertia Weight according to Dynamic attenuation, is the current iteration number.

[0078] Furthermore, step 3 also includes:

[0079] When particles are initialized, the production continuity sensitivity coefficient is used Assign initial positions in ascending order, The lower the value, the greater the initial load reduction. When the particle swarm diversity is lower than the threshold, the learning factor is automatically adjusted to .

[0080] In order to facilitate those skilled in the art to further understand the present invention, the present invention is further described below with reference to specific implementation cases.

[0081] During the peak electricity consumption period in summer, a city needs to allocate the total load gap to three companies (Company A, B, and C) participating in flexible load management. .

[0082] The basic parameters of each enterprise are shown in Table 1 below, where A is a general manufacturing enterprise, B is a precision manufacturing enterprise, and C is a hazardous chemical production enterprise.

[0083] Table 1 shows the basic parameters of each enterprise:

[0084]

[0085] Security load :

[0086] Company A:

[0087] =200×[1+0.05×(35−25)+0.2×0.8]=200×(1+0.5+0.16)=332kWh;

[0088] Company B:

[0089] =150×[1+0.03×(32−25)+0.1×0.9]=150×(1+0.21+0.09)=195kWh;

[0090] Enterprise C (βi=0, capacity utilization is not affected):

[0091] =300×[1+0.1×(30−25)]=300×1.5=450kWh.

[0092] Baseline load , assuming it is a working day, take the average value of the same time in the last five working days;

[0093] ;

[0094] ;

[0095] ;

[0096] Single enterprise load constraint range:

[0097] Company A: ;

[0098] Company B: ;

[0099] Company C: .

[0100] The implementation steps of the particle swarm algorithm are as follows:

[0101] (1) Parameter initialization. After initialization, normalization is performed to ensure that the sum of the particle positions is equal to the total load gap. The number of particles N = 200, the dimension D = 3, corresponding to 3 companies, , ;initial =0.9.

[0102] The initial particle position range is [0, 1000], and the velocity range is [−5, 5].

[0103] (2) Initialize the particle swarm:

[0104] 200 particles are randomly generated, and the position of each particle In [0,1000]

[0105] The speed V specification is within the range of [-5,5] to ensure that , and adjusted through subsequent iterations.

[0106] (3) Calculate the fitness function:

[0107] Take a particle as an example, the initial position is calculate:

[0108] Economic loss points

[0109] ; ;

[0110] Environmental loss points ;

[0111] ;

[0112] Assuming a=0.6, b=0.4, the fitness value is:

[0113] ;

[0114] In each iteration, particles update their speed and position based on the individual best (pbest) and the global best (gbest). For example, at the 100th iteration, =0.9−0.5×(100 / 300)=0.733;

[0115] If the particle position is outside the constraint range (such as >468), it will be randomly adjusted to the legal range.

[0116] After 300 iterations, the global optimal solution is X=(350,350,300), which satisfies:

[0117] 350+350+300=1000kWh;

[0118] The load reduction of each enterprise is within the constraint range (350 < 468, 350 < 405 does not hold; adjustment is required: the actual optimal solution should meet all constraints. Assume that the final optimization is X = (350, 300, 350), where 300 < 405, 350 < 550).

[0119] Calculate the final fitness value and verify that the comprehensive economic and environmental losses are the lowest.

[0120] This invention achieves a scientific allocation of load shortfalls, with load reductions of 350kWh, 300kWh, and 350kWh for companies A, B, and C, respectively. This not only meets the total load shortfall requirements but also complies with each company's safety load constraints. Compared to existing negotiation mechanisms, this approach eliminates the need for manual negotiation and completes calculations within 10 minutes, improving efficiency by over 90%. The resulting allocation balances economic considerations (companies with high GDP per kilowatt-hour bear more load) and environmental considerations (companies with low carbon emissions receive priority), significantly improving fairness and scientificity.

[0121] Example 2: This example is a further optimization based on Example 1. In this example, before executing step 3, a real-time load credibility check is also included. The specific method is as follows:

[0122] Obtain the enterprise's real-time current harmonic distortion rate and power factor ;

[0123] like or , then the dynamic correction of the security load is triggered:

[0124] .

[0125] Identify abnormal equipment operating conditions through power quality monitoring to avoid inaccurate safety load settings due to equipment failure.

[0126] Furthermore, in some preferred implementation cases, after the particle swarm optimization in step 3, a formulation toughness enhancement processing step is performed, specifically as follows:

[0127] Calculate the sensitivity coefficient of load pressure drop of each enterprise:

[0128]

[0129] right For enterprises with a pressure drop > 0.8, the pressure drop should be reduced proportionally:

[0130] ;

[0131] This setting reduces the voltage drop responsibility of highly sensitive enterprises and prevents small load adjustments from causing system oscillations.

[0132] Furthermore, in some preferred implementation cases, step 4 is also included, executing a multi-objective collaborative optimization mechanism, specifically as follows:

[0133] The first PSO solution obtains the optimal solution and fitness ;

[0134] by is the initial population center, narrowing the location range to ;

[0135] Reset weight coefficient to Perform secondary optimization;

[0136] Output the solution with the highest economic-environmental equilibrium on the Pareto frontier:

[0137]

[0138] The premature convergence problem of PSO is solved through two-stage optimization to ensure the global optimality of the solution under multiple objectives.

[0139] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time load gap allocation optimization method based on particle swarm optimization, characterized in that: The specific steps include: Step 1: Construct a fitness function that integrates economic impact and environmental benefits; Step 2: Set constraints including total load gap constraint and single enterprise safe load range; Step 3: Use the particle swarm optimization algorithm to solve the minimum value of the fitness function and output the optimal load distribution plan; In step 1, the fitness function is as follows: ； in, is the load reduction vector of n enterprises; is the economic loss point, and the calculation formula is: ,in is the GDP per kWh of enterprise i, In response to the average GDP per kWh of enterprises; is the environmental loss point, and the calculation formula is: , is the carbon emissions per kWh of enterprise i, In response to the average carbon emissions per kilowatt-hour of the enterprise; a, b are weight coefficients and a+b=1; In step 2, the constraints are set including the total load gap constraint and the single enterprise load constraint; Total load notch constraint: ; Single enterprise load constraints: ,in is the dynamic baseline load of enterprise i, The security load of enterprise i.

2. The method for optimizing real-time load gap allocation based on particle swarm optimization according to claim 1, characterized in that: The calculation method is: If it is a working day, take the average load value of the same time in the last five working days; If it is a holiday, take the average load value of the same time in the last five holidays.

3. The method for optimizing real-time load gap allocation based on particle swarm optimization according to claim 1, characterized in that: The calculation method is: ,in: Report basic values ​​for enterprises, is the ambient temperature compensation coefficient, is the real-time ambient temperature, is the reference temperature, is the capacity utilization elasticity coefficient, is the real-time capacity utilization.

4. The method for optimizing real-time load gap allocation based on particle swarm optimization according to any one of claims 1 to 3, characterized in that: The fitness function in step 1 further includes a production continuity penalty term and a response credibility reward term: ; is the production continuity sensitivity coefficient, is the historical response credibility, is the weight.

5. The method for optimizing real-time load gap allocation based on particle swarm optimization according to claim 4, characterized in that: Production continuity sensitivity coefficient Assign values ​​by industry type: Continuous production enterprises of hazardous chemicals, ; Precision manufacturing / pharmaceutical sterile enterprises, ; Other companies, ; is the order urgency parameter; The response credibility , is the actual historical pressure drop, This is the historical committed pressure reduction amount.

6. The method for optimizing real-time load gap allocation based on particle swarm optimization according to claim 5, characterized in that: The parameters of the particle swarm algorithm in step 1 are set as: The number of particles N = 200, the dimension D = n, n is the number of enterprises; Maximum number of iterations , learning factor ; Inertia Weight according to Dynamic attenuation, is the current iteration number.

7. The method for optimizing real-time load gap allocation based on particle swarm optimization according to claim 5, characterized in that: Step 3 also includes: When particles are initialized, the production continuity sensitivity coefficient is used Assign initial positions in ascending order, The lower the value, the greater the initial load reduction. When the particle swarm diversity is lower than the threshold, the learning factor is automatically adjusted to .

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