A distributed energy storage peak shaving method based on an improved equalization optimization algorithm

Abstract

The application discloses a distributed energy storage peak regulation method based on an improved balanced optimization algorithm, a model containing carbon emission is established, optimal peak regulation control is realized by taking total operation cost as an optimal control target, an optimal solution of the model is obtained by using the improved balanced optimization algorithm, and is converted into a peak regulation control strategy and sent to a virtual power plant for peak regulation. The peak regulation method can consider the carbon emission cost in the operation cost of the power grid, and is beneficial to realizing low-carbon operation of the power grid. The improved balanced optimization algorithm is used to solve the peak regulation model, the convergence precision and speed of the algorithm are ensured by improving population initialization and concentration updating formula. In addition, the application can increase the accommodation capacity of the power grid to renewable energy and improve the economy of power grid operation by optimizing the total operation cost.

Description

Technical Field

[0001] This invention relates to the field of power grid peak shaving technology, and in particular to a distributed energy storage peak shaving method based on an improved equilibrium optimization algorithm. Background Technology

[0002] Distributed energy storage features fast response, flexibility, and ease of precise control and bidirectional regulation. Power systems can participate in demand response by installing distributed energy storage devices, achieving peak shaving and valley filling, reducing the grid's peak-shaving pressure, and improving the grid's ability to absorb new energy sources and its operational efficiency. Currently, there are three main methods involving distributed energy storage for peak shaving:

[0003] An aggregation method for distributed energy storage to participate in system peak demand response (ZL202211512482.X): This paper presents an aggregation method for distributed energy storage to participate in system peak demand response. For different types of distributed energy storage resources, a hierarchical and partitioned aggregation topology is adopted. This provides an effective technical means for distributed energy storage resources to participate in the grid system peak demand response in response to grid demand. At the same time, a method for prioritizing the urgency of equipment response is proposed to ensure the reliability of the aggregated resources of vehicle-mounted energy storage groups in terms of timing and scale.

[0004] Strategy for Distributed Energy Storage Aggregation to Participate in Grid Peak Shaving under Multi-Energy Internet (ZL 202111552265.9): This paper provides a method for coordinated operation of distributed energy storage aggregation in the power grid. By establishing an overall control and operation framework for distributed energy storage to participate in grid peak shaving, a multi-objective optimization model for the coordinated operation of distributed energy storage aggregation and multi-energy is established. Based on heuristic intelligent algorithms, the multi-objective optimization model for the coordinated operation of distributed energy storage aggregation and multi-energy is optimized and solved in stages.

[0005] A frequency regulation and peak shaving distributed energy storage system and its frequency regulation and peak shaving method (ZL 202210527837.6): A frequency regulation and peak shaving distributed energy storage system and its frequency regulation and peak shaving method are provided, which includes a photovoltaic power generation array, a solar controller, an energy storage device, an inverter, a grid connection monitoring device and a home terminal control device. The system determines the working mode based on the grid frequency and participates in the frequency regulation and peak shaving of the grid.

[0006] The aforementioned invention patents primarily address how distributed energy storage can be aggregated for peak shaving and how individual energy storage devices can participate in grid peak shaving and frequency regulation. However, none of them consider carbon emission costs when distributed energy storage participates in peak shaving. Therefore, how to utilize power sources with regulating capabilities, such as hydropower and distributed energy storage, to reduce carbon emission costs while shaving peaks and filling valleys has become an urgent problem to be solved. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes a distributed energy storage peak-shaving method based on an improved equilibrium optimization algorithm.

[0008] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0009] A distributed energy storage peak-shaving method based on an improved equilibrium optimization algorithm includes the following steps:

[0010] 1) Establish a mathematical model framework for distributed energy storage and the participation of various power plants in grid peak shaving;

[0011] 2) Collect relevant information according to the mathematical models of distributed energy storage and various power plants to obtain the peak-shaving potential of distributed energy storage power stations and various power plants;

[0012] 3) Based on the constraints of power grid operation, establish an optimization model that minimizes total cost;

[0013] 4) Solve for the optimal solution of the optimization model and issue the peak-shaving strategy for distributed energy storage.

[0014] As one possible implementation, further, the mathematical model framework for distributed energy storage and various power plants participating in grid peak shaving in step 1) includes:

[0015] A) The mathematical model for electrochemical energy storage is as follows:

[0016]

[0017] In the formula: P C,n (t), P D,n (t) represents the charging and discharging power of the nth electrochemical energy storage power station during time period t; η represents the maximum charge / discharge power of the nth electrochemical energy storage power station during time period t. C,n η D,n Let SOC be the charge / discharge efficiency of the nth electrochemical energy storage power station. n (t) represents the capacity of the nth electrochemical energy storage power station during time period t, and SOC. n,min SOC n,max This represents the upper and lower limits of the capacity of the nth electrochemical energy storage power station;

[0018] B) The mathematical model for hydrogen energy storage is as follows:

[0019]

[0020] In the formula: P E,H,n (t) represents the hydrogen energy output of the nth electrolyzer during time period t, P E,n (t) represents the electrical energy input to the electrolyzer of the nth hydrogen energy storage power station during time period t, η E,nLet be the energy conversion efficiency of the electrolyzer in the nth hydrogen energy storage power station. These represent the upper and lower limits of the electrical energy input to the electrolyzer of the nth hydrogen energy storage power station, respectively. These represent the upper and lower limits of the ramp rate for the electrolyzer of the nth hydrogen energy storage power station; P H,E,n (t) represents the electrical energy output of the nth hydrogen energy storage power station during time period t, P H,H,n (t) represents the thermal energy output of the nth hydrogen energy storage power station during time period t, η HE,n η HH,n Let P be the efficiency of converting hydrogen into electricity and heat energy in the nth hydrogen energy storage power station, respectively. H,n (t) represents the hydrogen energy input to the nth hydrogen energy storage power station during time period t. The upper and lower limits of the input electrical energy of the nth hydrogen energy storage power station, respectively. These represent the upper and lower limits of the ramp rate for the nth hydrogen energy storage power station; The upper and lower limits of the heat-to-power ratio of the nth hydrogen energy storage power station;

[0021] C) The mathematical model of a combined heat and power (CHP) plant is as follows:

[0022]

[0023] In the formula: P N,E,n (t) represents the electrical energy output of the nth combined heat and power plant during time period t, P N,H,n (t) represents the thermal energy output of the nth cogeneration power plant during time period t, P N,n (t) represents the natural gas power input to the nth cogeneration power plant during time period t. Let be the efficiency of the natural gas to electricity conversion in the nth combined heat and power (CHP) plant. Let be the efficiency of the natural gas to heat energy conversion in the nth combined heat and power plant. These represent the upper and lower limits of the input natural gas power of the nth cogeneration power plant. These represent the upper and lower limits of the ramp rate for the nth combined heat and power plant. The upper and lower limits of the heat-to-power ratio for the nth combined heat and power plant; P N,H,n (t) represents the hydrogen energy input to the methane reactor of the nth cogeneration power plant during time period t, P H,N,n (t) represents the natural gas output power of the methane reactor in the nth cogeneration power plant during time period t. Let be the efficiency of the methane reactor in the nth combined heat and power plant in converting hydrogen into natural gas. These are the upper and lower limits of the hydrogen energy input to the methane reactor of the nth combined heat and power plant. These are the upper and lower limits of the ramp-up speed of the methane reactor in the nth cogeneration power plant;

[0024] D) The mathematical model of a pumped storage power station is as follows:

[0025]

[0026] In the formula: W n (t) represents the power stored in the upper reservoir of the nth pumped storage power station during time period t. Let P be the upper and lower limits of the upper reservoir storage capacity of the nth pumped storage power station. wc,n (t), P wd,n (t) represents the charging and discharging power of the nth pumped storage power station during time period t, η w,n η g,n Let be the charging and discharging efficiency of the nth pumped storage power station. These are the upper and lower limits of the charging power of the nth pumped storage power station, respectively. α1 and α2 represent the upper and lower limits of the discharge power of the nth pumped storage power station, respectively, and the upper and lower margin coefficients are the upper and lower margin coefficients, respectively. When both are 1, it represents the conventional synchronous optimization scheduling model.

[0027] As one possible implementation, further, in step 2), relevant information of the mathematical models of electrochemical energy storage power stations, hydrogen energy storage power stations, combined heat and power power stations, and pumped storage power stations is collected.

[0028] As one possible implementation, the electrochemical energy storage power station further collects information including: basic data such as energy storage resource scale, real-time charging and discharging power, real-time charging and discharging capacity, and time-of-use power calculation; and analyzes the remaining charging and discharging capacity and charging and discharging power of each electrochemical energy storage power station based on SOC and SOH information.

[0029] The information collected by the hydrogen energy storage power station includes: available electrical or hydrogen energy, upper and lower limits of available electrical or hydrogen energy, efficiency of converting hydrogen energy into electrical energy and efficiency of converting electrical energy into hydrogen energy, upper and lower limits of hydrogen energy and electrical energy ramp-up, and upper and lower limits of heat-to-electricity ratio.

[0030] The information collected by the cogeneration power plant includes: hydrogen input power, hydrogen-to-natural gas conversion efficiency, upper and lower limits of hydrogen input power and ramp rate, natural gas input power, natural gas-to-electricity and-heat conversion efficiency, upper and lower limits of natural gas input power and ramp power, and upper and lower limits of electro-thermal ratio.

[0031] The information collected by the pumped storage power station includes: the water level stored in the upper reservoir, the upper and lower limit water levels, the power generation capacity of the generator, and the pumping capacity of the pump, and converts the water level of the upper reservoir into stored energy and upper and lower limit energy.

[0032] As one possible implementation, further, the constraints on grid operation in step 3) include: the operating constraints of the distributed energy storage power station are shown in formulas (1) to (4), and the remaining operating constraints are as follows:

[0033] I) Output constraints of renewable energy:

[0034]

[0035] In the formula: This is the upper limit of the output power of wind power generation. This represents the upper limit of the output power of photovoltaic power generation.

[0036] II) Power balance constraints:

[0037] P3(t)=P l (t)+P E (t)+P C (t)+P W (t)+P N,H (t)-P DG,w (t)-P DG,p (t)-P H,E (t)-P N,E (t)-P1(t)-P2(t) (13)

[0038] In the formula: P l (t) represents the electrical load of the local power grid during time period t, P E (t) represents the electrical power input to all electrolyzers of the hydrogen energy storage power station during time period t, P C (t) represents the electrical power input to all electrochemical energy storage power stations, P W (t) represents the electrical power input to the pumped storage power station, P N,H (t) represents the electrical power input to all combined heat and power plants, P H,E (t) represents the electrical power output of all hydrogen energy storage power stations during time period t, P N,E P(t) represents the total power output of all cogeneration plants during time period t; P1(t) represents the thermal power generation of the local power grid during time period t; P2(t) represents the power generation of the remaining plants in the local power grid during time period t; P3(t) represents the amount of electricity purchased by the local power grid from the upstream power grid and the price thereon. If P3(t) is negative, then electricity is fed back to the upstream power grid.

[0039] III) Thermal power balance constraint:

[0040] P H,H (t)+P H,N (t)=P h (t) (14)

[0041] In the formula: PH,H (t) represents the thermal power output of all hydrogen energy storage power stations during time period t, P H,N (t) represents the gross heat output of all cogeneration plants during time period t, P h (t) represents the heat load during time period t;

[0042] IV) Natural Gas Balance Constraints

[0043] P b (t)=P g (t)+P N (t)-P H,N (t) (15)

[0044] In the formula: P b (t) represents the natural gas purchased during time period t, P g (t) represents the natural gas load during time period t, P N (t) represents the natural gas power consumed by all cogeneration plants during time period t, P H,N (t) represents the natural gas output from the methane reactor of all cogeneration power plants during time period t;

[0045] V) Hydrogen balance constraint

[0046] P E,H (t)=P H (t)+P N,H (t) (16)

[0047] In the formula: P E,H (t) represents the hydrogen power output from all the electrolyzers of the hydrogen energy storage power station during time period t, P H (t) represents the hydrogen power consumed by all hydrogen energy storage power stations during time period t, P N,H (t) represents the hydrogen power consumed by all cogeneration power plants during time period t.

[0048] As one possible implementation, further, in step 3), an optimization model that minimizes the total cost is established, specifically as follows:

[0049] The total cost is calculated using electricity purchase cost, carbon emission cost, and wind and solar curtailment cost as the total cost, and the objective function is to minimize the total cost.

[0050] The objective function is expressed as follows:

[0051] F p =min(f p +f c +f DG,w +f DG,p (5)

[0052] In the formula: F p f represents the total operating cost. pFor the power grid's electricity purchase cost, f c For carbon emission costs, f DG,w For the cost of wind curtailment in wind power generation, f DG,p The cost of curtailment of photovoltaic power generation;

[0053] The electricity purchase cost of the power grid is as follows:

[0054]

[0055] In the formula: P1 and M1 are the thermal power generation and grid connection price of the local power grid, respectively; P2 and M2 are the power generation and grid connection price of the other power plants in the local power grid, respectively; P3 and M3 are the electricity and price purchased by the local power grid from the higher-level power grid, respectively; if P3 is negative, the electricity is fed back to the higher-level power grid.

[0056] The carbon emission costs are as follows:

[0057]

[0058] Formula: E c The total carbon emissions are calculated from the carbon emissions from purchasing electricity from higher levels, E. 3c Carbon emissions E from power generation by other power plants in this grid 2c Total carbon emissions E from distributed energy storage t The composition of carbon dioxide absorbed by the methane reactor; a3, b3, and c3 are the carbon emission coefficients of coal-fired units in the upstream power grid, a2, b2, and c2 are the carbon emission coefficients of coal-fired units in the local power grid, and a1, b1, and c1 are the carbon emission coefficients of natural gas-consuming units, P t (t) represents the thermal energy P output by all cogeneration plants during time period t. N,H (t) and P output from all hydrogen fuel cell power plants H,H (t); ω is the calculated parameter for carbon dioxide absorption in the methane reactor, P H,N (t) represents the natural gas power output from the methane reactor;

[0059] The actual total carbon emissions E are obtained from the above formula. c Based on the carbon emission credits of the local power grid, the carbon emission credits required to participate in the carbon emission trading market are calculated as follows:

[0060] E tt =E c -E a (8)

[0061] In the formula: E tt For the carbon emission credits actually used to participate in the carbon emission trading market, E a Carbon emission allowances allocated to this level of power grid;

[0062] The carbon emission cost can be obtained from the above formula:

[0063] f c =E tt ×f price (9)

[0064] In the formula: f price The price per unit of carbon emission credit can be adjusted according to the actual market.

[0065] The costs of curtailing wind and solar power are as follows:

[0066] Wind curtailment cost f DG,w The details are as follows:

[0067]

[0068] Where: δ w The cost of wind curtailment penalty per unit; P DG,w (t) represents the wind curtailment power during time period t.

[0069] Cost of abandoned light f DG,p The details are as follows:

[0070]

[0071] Where: δ p The cost of per unit of abandoned light penalty; P DG,p (t) represents the abandoned light power during time period t.

[0072] As one possible implementation, further, in step 4), the improved equilibrium optimization algorithm is used to solve the optimal solution of the optimization model, and the peak-shaving strategy of distributed energy storage is issued.

[0073] As one possible implementation, the improved equilibrium optimization algorithm further generates an initial population by initializing the population with a set of optimal points, as shown in the following formula:

[0074] P n =[P n (1),P n (2),…,P n (i)], i=1,2,…,n (17)

[0075] P n (i)=({l1×i},{l2×i},…,{l m ×i}) (18)

[0076]

[0077] In the formula, P nLet p be a set of n points in m-dimensional space, where p is the smallest prime number satisfying (pm) / 2≥m. Then the concentration of the i-th individual is:

[0078]

[0079] Among them I i It is the concentration of the i-th individual, I max and I min These are the upper and lower bounds for optimization. Calculate the fitness of the initialized population, select the four concentrations with the best fitness and the mean of these four concentrations, and place these five individuals into the equalization pool, as shown in the formula:

[0080] I pool =[I1,I2,I3,I4,I avg ] (twenty one)

[0081] I eq =rand(I pool (22)I eq This represents a candidate solution randomly selected from the equilibrium pool with equal probability.

[0082] As a possible implementation, the improved equilibrium optimization algorithm further introduces nonlinear adaptive weights into the basic equilibrium optimization algorithm concentration update formula to balance the global search capability and local search capability of the algorithm during the iteration process. The improved formula is as follows:

[0083]

[0084] In the formula: λ is a random vector in the range [0,1], V represents the unit volume; D is the exponential term, F is the generation probability; ite is the current iteration number; Maxite is the maximum iteration number; the convergence factor β starts from the maximum value β max Nonlinear decay to minimum value β min y is an adjustment parameter that determines the rate of change of β;

[0085] The definition of the exponent term D is as follows:

[0086] D = a1sign(r-0.5)(e -λs -1) (26)

[0087]

[0088] In the formula: a1 is a constant used to control the global search capability; a2 is a constant used to control the local search capability; sign is the sign function, which determines the direction of D; r represents a random number; and s is a nonlinear factor that decreases with each iteration.

[0089] The generation probability F is defined as follows:

[0090] F = F0e -λ(s-s0) (28)

[0091]

[0092] F0 = FCP(I) eq -λI i (30)

[0093]

[0094] In the formula: F0 mainly controls whether the particle uses FCP to update the state, FP determines the form of the particle update state, s0 is to improve the global search capability of the algorithm while reducing the search speed, and R1 and R2 are random numbers.

[0095] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0096] The distributed energy storage peak-shaving method based on the modified equalization optimization algorithm provided in this invention can obtain the optimal solution by utilizing the improved equalization optimization algorithm, taking into account carbon emission costs, grid operation costs, and the costs of curtailing wind and solar power. While minimizing the total cost, it increases the grid's capacity to accommodate renewable energy and improves the economic efficiency of grid operation. Attached Figure Description

[0097] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0098] Figure 1 This is a simplified flowchart of the present invention;

[0099] Figure 2 A simplified flowchart of the improved equilibrium optimization algorithm. Detailed Implementation

[0100] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0101] See attached document Figure 1As shown, this invention provides a distributed energy storage peak-shaving method based on an improved equilibrium optimization algorithm, comprising the following steps:

[0102] 1) Establish a mathematical model framework for distributed energy storage and the participation of various power plants in grid peak shaving; the mathematical model framework for distributed energy storage and the participation of various power plants in grid peak shaving includes:

[0103] A) The mathematical model for electrochemical energy storage is as follows:

[0104]

[0105] In the formula: P C,n (t), P D,n (t) represents the charging and discharging power of the nth electrochemical energy storage power station during time period t; η represents the maximum charge / discharge power of the nth electrochemical energy storage power station during time period t. C,n η D,n Let SOC be the charge / discharge efficiency of the nth electrochemical energy storage power station. n (t) represents the capacity of the nth electrochemical energy storage power station during time period t, and SOC. n,min SOC n,max This represents the upper and lower limits of the capacity of the nth electrochemical energy storage power station;

[0106] B) The mathematical model for hydrogen energy storage is as follows:

[0107]

[0108] In the formula: P E,H,n (t) represents the hydrogen energy output of the nth electrolyzer during time period t, P E,n (t) represents the electrical energy input to the electrolyzer of the nth hydrogen energy storage power station during time period t, η E,n Let be the energy conversion efficiency of the electrolyzer in the nth hydrogen energy storage power station. These represent the upper and lower limits of the electrical energy input to the electrolyzer of the nth hydrogen energy storage power station, respectively. These represent the upper and lower limits of the ramp rate for the electrolyzer of the nth hydrogen energy storage power station; P H,E,n (t) represents the electrical energy output of the nth hydrogen energy storage power station during time period t, P H,H,n (t) represents the thermal energy output of the nth hydrogen energy storage power station during time period t, η HE,n η HH,n Let P be the efficiency of converting hydrogen into electricity and heat energy in the nth hydrogen energy storage power station, respectively. H,n (t) represents the hydrogen energy input to the nth hydrogen energy storage power station during time period t. The upper and lower limits of the input electrical energy of the nth hydrogen energy storage power station, respectively. These represent the upper and lower limits of the ramp rate for the nth hydrogen energy storage power station; The upper and lower limits of the heat-to-power ratio of the nth hydrogen energy storage power station;

[0109] C) The mathematical model of a combined heat and power (CHP) plant is as follows:

[0110]

[0111] In the formula: P N,E,n (t) represents the electrical energy output of the nth combined heat and power plant during time period t, P N,H,n (t) represents the thermal energy output of the nth cogeneration power plant during time period t, P N,n (t) represents the natural gas power input to the nth cogeneration power plant during time period t. Let be the efficiency of the natural gas to electricity conversion in the nth combined heat and power (CHP) plant. Let be the efficiency of the natural gas to heat energy conversion in the nth combined heat and power plant. These represent the upper and lower limits of the input natural gas power of the nth cogeneration power plant. These represent the upper and lower limits of the ramp rate for the nth combined heat and power plant. The upper and lower limits of the heat-to-power ratio for the nth combined heat and power plant; P N,H,n (t) represents the hydrogen energy input to the methane reactor of the nth cogeneration power plant during time period t, P H,N,n (t) represents the natural gas output power of the methane reactor in the nth cogeneration power plant during time period t. Let be the efficiency of the methane reactor in the nth combined heat and power plant in converting hydrogen into natural gas. These are the upper and lower limits of the hydrogen energy input to the methane reactor of the nth combined heat and power plant. These are the upper and lower limits of the ramp-up speed of the methane reactor in the nth cogeneration power plant;

[0112] D) The mathematical model of a pumped storage power station is as follows:

[0113]

[0114] In the formula: W n (t) represents the power stored in the upper reservoir of the nth pumped storage power station during time period t. Let P be the upper and lower limits of the upper reservoir storage capacity of the nth pumped storage power station. wc,n (t), P wd,n (t) represents the charging and discharging power of the nth pumped storage power station during time period t, η w,n η g,n Let be the charging and discharging efficiency of the nth pumped storage power station. These are the upper and lower limits of the charging power of the nth pumped storage power station, respectively. α1 and α2 represent the upper and lower limits of the discharge power of the nth pumped storage power station, respectively, and the upper and lower margin coefficients are the upper and lower margin coefficients, respectively. When both are 1, it represents the conventional synchronous optimization scheduling model.

[0115] 2) Collect relevant information based on the mathematical models of distributed energy storage and various power plants to obtain the peak-shaving potential of distributed energy storage power stations and various power plants. In this embodiment, Internet + AI technology is used to collect relevant information based on the mathematical models of distributed energy storage and various power plants, and to obtain the peak-shaving potential (i.e., the amount of electricity that can be mobilized) of distributed energy storage power stations and various power plants. Specifically, relevant information from the mathematical models of electrochemical energy storage power stations, hydrogen energy storage power stations, combined heat and power power stations, and pumped storage power stations is collected.

[0116] The information collected by the electrochemical energy storage power station includes: basic data such as energy storage resource scale, real-time charging and discharging power, real-time charging and discharging capacity, and time-of-use power calculation; and based on SOC and SOH information, the remaining charging and discharging capacity and charging and discharging power of each electrochemical energy storage power station are analyzed.

[0117] The information collected by the hydrogen energy storage power station includes: available electrical or hydrogen energy, upper and lower limits of available electrical or hydrogen energy, efficiency of converting hydrogen energy into electrical energy and efficiency of converting electrical energy into hydrogen energy, upper and lower limits of hydrogen energy and electrical energy ramp-up, and upper and lower limits of heat-to-power ratio.

[0118] Information collected by the cogeneration power plant includes: hydrogen input power, hydrogen-to-natural gas conversion efficiency, upper and lower limits of hydrogen input power and ramp rate, natural gas input power, natural gas-to-electricity and-heat conversion efficiency, upper and lower limits of natural gas input power and ramp rate, and upper and lower limits of the power-to-heat ratio.

[0119] The information collected by the pumped storage power station includes: the water level stored in the upper reservoir, the upper and lower limit water levels, the power generation capacity of the generator, and the pumping capacity of the pump, and converts the water level of the upper reservoir into stored energy and upper and lower limit energy.

[0120] 3) Considering the constraints of power grid operation, establish an optimization model that minimizes total cost; the optimization model that minimizes total cost is established with the total cost of electricity purchase cost, carbon emission cost, wind curtailment cost and solar curtailment cost as the total cost, and the objective function is to minimize total cost.

[0121] The objective function is expressed as follows:

[0122] F p =min(f p +f c +f DG,w +f DG,p (5)

[0123] In the formula: F p f represents the total operating cost.p For the power grid's electricity purchase cost, f c For carbon emission costs, f DG,w For the cost of wind curtailment in wind power generation, f DG,p The cost of curtailment of photovoltaic power generation;

[0124] The electricity purchase cost of the power grid is as follows:

[0125]

[0126] In the formula: P1 and M1 are the thermal power generation and grid connection price of the local power grid, respectively; P2 and M2 are the power generation and grid connection price of the other power plants in the local power grid, respectively; P3 and M3 are the electricity and price purchased by the local power grid from the higher-level power grid, respectively; if P3 is negative, the electricity is fed back to the higher-level power grid.

[0127] The carbon emission costs are as follows:

[0128]

[0129] Formula: E c The total carbon emissions are calculated from the carbon emissions from purchasing electricity from higher levels, E. 3c Carbon emissions E from power generation by other power plants in this grid 2c Total carbon emissions E from distributed energy storage t The composition of carbon dioxide absorbed by the methane reactor; a3, b3, and c3 are the carbon emission coefficients of coal-fired units in the upstream power grid, a2, b2, and c2 are the carbon emission coefficients of coal-fired units in the local power grid, and a1, b1, and c1 are the carbon emission coefficients of natural gas-consuming units, P t (t) represents the thermal energy P output by all cogeneration plants during time period t. N,H (t) and P output from all hydrogen fuel cell power plants H,H (t); ω is the calculated parameter for carbon dioxide absorption in the methane reactor, P H,N (t) represents the natural gas power output of the methane reactor during time period t;

[0130] The actual total carbon emissions E are obtained from the above formula. c Based on the carbon emission credits of the local power grid, the carbon emission credits required to participate in the carbon emission trading market are calculated as follows:

[0131] E tt =E c -E a (8)

[0132] In the formula: E tt For the carbon emission credits actually used to participate in the carbon emission trading market, E a Carbon emission allowances allocated to this level of power grid;

[0133] The carbon emission cost can be obtained from the above formula:

[0134] f c =E tt ×f price (9)

[0135] In the formula: f price The price per unit of carbon emission credit can be adjusted according to the actual market.

[0136] The costs of curtailing wind and solar power are as follows:

[0137] Wind curtailment cost f DG,w The details are as follows:

[0138]

[0139] Where: δ w The cost of wind curtailment penalty per unit; P DG,w (t) represents the wind curtailment power during time period t.

[0140] Cost of abandoned light f DG,p The details are as follows:

[0141]

[0142] Where: δ p The cost of per unit of abandoned light penalty; P DG,p (t) represents the abandoned light power during time period t.

[0143] The constraints on grid operation include the operating constraints of the distributed energy storage power station, which are shown in formulas (1) to (4). The remaining operating constraints are as follows:

[0144] I) Output constraints of renewable energy:

[0145]

[0146] In the formula: This is the upper limit of the output power of wind power generation. This represents the upper limit of the output power of photovoltaic power generation.

[0147] II) Power balance constraints:

[0148] P3(t)=P l (t)+P E (t)+P C (t)+P W (t)+P N,H (t)-P DG,w (t)-P DG,p (t)-P H,E (t)-P N,E(t)-P1(t)-P2(t) (13)

[0149] In the formula: P l (t) represents the electrical load of the local power grid during time period t, P E (t) represents the electrical power input to all electrolyzers of the hydrogen energy storage power station during time period t, P C (t) represents the electrical power input to all electrochemical energy storage power stations during time period t, P W (t) represents the electrical power input to the pumped storage power station during time period t, P N,H (t) represents the electrical power input to all cogeneration plants during time period t, P H,E (t) represents the electrical power output of all hydrogen energy storage power stations during time period t, P N,E P(t) represents the total power output of all cogeneration plants during time period t; P1(t) represents the thermal power generation of the local power grid during time period t; P2(t) represents the power generation of the remaining plants in the local power grid during time period t; P3(t) represents the amount of electricity purchased by the local power grid from the upstream power grid and the price thereon. If P3(t) is negative, then electricity is fed back to the upstream power grid.

[0150] III) Thermal power balance constraint:

[0151] P H,H (t)+P H,N (t)=P h (t) (14)

[0152] In the formula: P H,H (t) represents the thermal power output of all hydrogen energy storage power stations during time period t, P H,N (t) represents the gross heat output of all cogeneration plants during time period t, P h (t) represents the heat load during time period t;

[0153] IV) Natural Gas Balance Constraints

[0154] P b (t)=P g (t)+P N (t)-P H,N (t) (15)

[0155] In the formula: P b (t) represents the natural gas purchased during time period t, P g (t) represents the natural gas load during time period t, P N (t) represents the natural gas power consumed by all cogeneration plants during time period t, P H,N (t) represents the natural gas power output from the methane reactors of all cogeneration power plants during time period t;

[0156] V) Hydrogen balance constraint

[0157] P E,H (t)=P H (t)+P N,H (t) (16)

[0158] In the formula: P E,H (t) represents the hydrogen power output from all the electrolyzers of the hydrogen energy storage power station during time period t, P H (t) represents the hydrogen power consumed by all hydrogen energy storage power stations during time period t, P N,H (t) represents the hydrogen power consumed by all cogeneration power plants during time period t.

[0159] 4) Solve for the optimal solution of the optimization model and issue the peak-shaving strategy for distributed energy storage. In this embodiment, an improved equilibrium optimization algorithm is used to solve for the optimal solution of the optimization model.

[0160] Like most metaheuristic algorithms, the basic Equilibrium optimization (EO) algorithm initializes its population using random generation. The quality of the initial population directly affects the algorithm's convergence accuracy and speed. Randomly generated populations are not uniform, especially in high-dimensional spaces, and this problem is more pronounced, failing to guarantee the diversity of the initial population. The improved EO algorithm uses a set of optimal points to initialize the initial population. This results in a population that is evenly distributed across high-dimensional spaces, further improving the algorithm's convergence accuracy and speed. The formula is as follows:

[0161] P n =[P n (1),P n (2),…,P n (i)], i=1,2,…,n (17)

[0162] P n (i)=({l1×i},{l2×i},…,{l m ×i}) (18)

[0163]

[0164] In the formula, P n Let p be a set of n points in m-dimensional space, where p is the smallest prime number satisfying (pm) / 2≥m. Then the concentration of the i-th individual is:

[0165]

[0166] Among them I i It is the concentration of the i-th individual, I max and I minThese are the upper and lower bounds for optimization. Calculate the fitness of the initialized population, select the four concentrations with the best fitness and the mean of these four concentrations, and place these five individuals into the equalization pool, as shown in the formula:

[0167] I pool =[I1,I2,I3,I4,I avg ] (twenty one)

[0168] I eq =rand(I pool (22)I eq This represents a candidate solution randomly selected from the equilibrium pool with equal probability.

[0169] Concentration update reflects the process of particles moving from their current position to another, and the basic concentration update formula for equilibrium optimization algorithms is as follows:

[0170]

[0171] λ is a random vector in the range [0,1], V represents the unit volume, D is the exponential term, F is the generation probability, the first term is the equilibrium concentration, the second term determines the global search capability, and the third term determines the local search capability.

[0172] Global and local search capabilities affect the convergence speed and optimization accuracy of an algorithm. The parameters in the basic equilibrium optimization algorithm are set empirically and are fixed values, leading to randomness and limitations. The improved equilibrium optimization algorithm introduces nonlinear adaptive weights into the concentration update formula to balance the global and local search capabilities during the iteration process. The improved formula is as follows:

[0173]

[0174]

[0175] In the formula: λ is a random vector in the range [0,1], V represents the unit volume; D is the exponential term, F is the generation probability; ite is the current iteration number; Maxite is the maximum iteration number; the convergence factor β starts from the maximum value β max Nonlinear decay to minimum value β min y is an adjustment parameter that determines the rate of change of β.

[0176] In the early stages of the algorithm, a larger convergence step size is used to give the algorithm enough search space to disperse the population and improve the algorithm's global search capability. In the middle and later stages of the algorithm, local search begins, and at this time, a smaller convergence step size is beneficial for the algorithm to explore local areas.

[0177] The definition of the exponent term D is as follows:

[0178] D = a1sign(r-0.5)(e -λs -1) (26)

[0179]

[0180] In the formula: a1 is a constant used to control the global search capability; a2 is a constant used to control the local search capability; sign is the sign function, which determines the direction of D; r represents a random number; and s is a nonlinear factor that decreases with each iteration.

[0181] The generation probability F is defined as follows:

[0182] F = F0e -λ(s-s0) (28)

[0183]

[0184] F0 = FCP(I) eq -λI i (30)

[0185]

[0186] In the formula: F0 mainly controls whether the particle uses FCP to update the state, FP determines the form of the particle update state, s0 is to improve the global search capability of the algorithm while reducing the search speed, and R1 and R2 are random numbers.

[0187] See attached document Figure 2 As shown, the improved equilibrium optimization algorithm flow is as follows:

[0188] S1: Generate the initial population by initializing the population using a set of optimal points;

[0189] S2: Determine if the current iteration count has reached the maximum value. If not, execute S3; if so, execute S7.

[0190] S3: Calculate the fitness of individuals in the population; specifically, substitute the individual's solution into the constraints and objective function to calculate the individual's fitness, as shown in the following formula:

[0191] Fit(I i ) = F P

[0192] S4: Select four optimal solutions based on the fitness calculation results and construct a balanced pool;

[0193] S5: Calculate and update parameters such as F, D, s, and β.

[0194] S6: Randomly select a candidate solution from the equilibrium pool and update the position of the individual in the population, then execute S2;

[0195] S7: Output the optimal solution.

[0196] The aforementioned peak-shaving method incorporates carbon emission costs into the grid's operating costs, facilitating low-carbon grid operation. An improved equilibrium optimization algorithm is used to solve the peak-shaving model; improvements to the population initialization and concentration update formulas ensure the algorithm's convergence accuracy and speed. By optimizing the calculation to minimize overall operating costs, the grid's capacity to accommodate renewable energy is increased, thus improving the economic efficiency of grid operation.

[0197] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A distributed energy storage peak-shaving method based on an improved equilibrium optimization algorithm, characterized in that, Includes the following steps: 1) Establish a mathematical model framework for distributed energy storage and the participation of various power plants in grid peak shaving; 2) Collect relevant information according to the mathematical models of distributed energy storage and various power plants to obtain the peak-shaving potential of distributed energy storage power stations and various power plants; 3) Based on the constraints of power grid operation, establish an optimization model that minimizes total cost; 4) Use the improved equilibrium optimization algorithm to solve the optimal solution of the optimization model and issue the peak-shaving strategy of distributed energy storage. The improved equilibrium optimization algorithm generates the initial population by initializing the population with a set of optimal points, and its formula is as follows: P n =[P n (1),P n (2),…,P n (i)],i=1,2,…,n (17) P n (i)=({l1×i},{l2×i},…,{l m ×i}) (18) In the formula, P n Let p be a set of n points in m-dimensional space, where p is the smallest prime number satisfying (pm) / 2≥m. Then the concentration of the i-th individual is: Among them I i It is the concentration of the i-th individual, I max and I min It refers to the upper and lower bounds for finding the optimal solution; Calculate the fitness of the initialized population, select the four concentrations with the best fitness and the mean of these four concentrations, and place these five individuals into the equalization pool, as shown in the formula below: I pool =[I1,I2,I3,I4,I avg ] (21) I eq =rand(I pool ) (22) I eq This represents candidate solutions randomly selected from the equilibrium pool with equal probability; The improved equilibrium optimization algorithm introduces nonlinear adaptive weights into the concentration update formula of the basic equilibrium optimization algorithm to balance the global search capability and local search capability of the algorithm during the iteration process. The improved formula is as follows: In the formula: λ is a random vector in the range [0,1], V represents the unit volume; D is the exponential term, F is the generation probability; ite is the current iteration number; Maxite is the maximum iteration number; the convergence factor β starts from the maximum value β max Nonlinear decay to minimum value β min y is an adjustment parameter that determines the rate of change of β; The definition of the exponent term D is as follows: D=a1sign(r-0.5)(e -λs -1) (26) In the formula: a1 is a constant used to control the global search capability; a2 is a constant used to control the local search capability; sign is the sign function, which determines the direction of D; r represents a random number; and s is a nonlinear factor that decreases with each iteration. The generation probability F is defined as follows: F=F0e -λ(s-s0) (28) F0=FCP(I eq -λI i ) (30) In the formula: F0 mainly controls whether the particle uses FCP to update the state, FP determines the form of the particle update state, s0 is to improve the global search capability of the algorithm while reducing the search speed, and R1 and R2 are random numbers.

2. The distributed energy storage peak-shaving method based on the improved equilibrium optimization algorithm according to claim 1, characterized in that, The mathematical model framework for distributed energy storage and various power plants participating in grid peak shaving in step 1) includes: A) The mathematical model for electrochemical energy storage is as follows: In the formula: P C,n (t), P D,n (t) represents the charging and discharging power of the nth electrochemical energy storage power station during time period t; η represents the maximum charge / discharge power of the nth electrochemical energy storage power station during time period t. C,n η D,n Let SOC be the charge / discharge efficiency of the nth electrochemical energy storage power station. n (t) represents the capacity of the nth electrochemical energy storage power station during time period t, and SOC. n,min SOC n,max This represents the upper and lower limits of the capacity of the nth electrochemical energy storage power station; B) The mathematical model for hydrogen energy storage is as follows: In the formula: P E,H,n (t) represents the hydrogen energy output of the nth electrolyzer during time period t, P E,n (t) represents the electrical energy input to the electrolyzer of the nth hydrogen energy storage power station during time period t, η E,n Let be the energy conversion efficiency of the electrolyzer in the nth hydrogen energy storage power station. These represent the upper and lower limits of the electrical energy input to the electrolyzer of the nth hydrogen energy storage power station, respectively. These represent the upper and lower limits of the ramp rate for the electrolyzer of the nth hydrogen energy storage power station; P H,E,n (t) represents the electrical energy output of the nth hydrogen energy storage power station during time period t, P H,H,n (t) represents the thermal energy output of the nth hydrogen energy storage power station during time period t, η HE,n η HH,n Let P be the efficiency of converting hydrogen into electricity and heat energy in the nth hydrogen energy storage power station, respectively. H,n (t) represents the hydrogen energy input to the nth hydrogen energy storage power station during time period t. The upper and lower limits of the input electrical energy of the nth hydrogen energy storage power station, respectively. These represent the upper and lower limits of the ramp rate for the nth hydrogen energy storage power station; The upper and lower limits of the heat-to-power ratio of the nth hydrogen energy storage power station; C) The mathematical model of a combined heat and power (CHP) plant is as follows: In the formula: P N,E,n (t) represents the electrical energy output of the nth combined heat and power plant during time period t, P N,H,n (t) represents the thermal energy output of the nth cogeneration power plant during time period t, P N,n (t) represents the natural gas power input to the nth cogeneration power plant during time period t. Let be the efficiency of the natural gas to electricity conversion in the nth combined heat and power (CHP) plant. Let be the efficiency of the natural gas to heat energy conversion in the nth combined heat and power plant. These represent the upper and lower limits of the input natural gas power of the nth cogeneration power plant. These represent the upper and lower limits of the ramp rate for the nth combined heat and power plant. The upper and lower limits of the heat-to-power ratio for the nth combined heat and power plant; P N,H,n (t) represents the hydrogen energy input to the methane reactor of the nth cogeneration power plant during time period t, P H,N,n (t) represents the natural gas output power of the methane reactor in the nth cogeneration power plant during time period t. Let be the efficiency of the methane reactor in the nth combined heat and power plant in converting hydrogen into natural gas. These are the upper and lower limits of the hydrogen energy input to the methane reactor of the nth combined heat and power plant. These are the upper and lower limits of the ramp-up speed of the methane reactor in the nth cogeneration power plant; D) The mathematical model of a pumped storage power station is as follows: In the formula: W n (t) represents the power stored in the upper reservoir of the nth pumped storage power station during time period t. Let P be the upper and lower limits of the upper reservoir storage capacity of the nth pumped storage power station. wc,n (t), P wd,n (t) represents the charging and discharging power of the nth pumped storage power station during time period t, η w,n η g,n Let be the charging and discharging efficiency of the nth pumped storage power station. These are the upper and lower limits of the charging power of the nth pumped storage power station, respectively. α1 and α2 are the upper and lower limits of the discharge power of the nth pumped storage power station, respectively, and the upper and lower margin coefficients are the upper and lower margin coefficients, respectively.

3. The distributed energy storage peak-shaving method based on the improved equilibrium optimization algorithm according to claim 1, characterized in that, Step 2) collects relevant information on the mathematical models of electrochemical energy storage power stations, hydrogen energy storage power stations, combined heat and power power stations, and pumped storage power stations.

4. The distributed energy storage peak-shaving method based on the improved equilibrium optimization algorithm according to claim 3, characterized in that, The information collected by the electrochemical energy storage power station includes: energy storage resource scale, real-time charging and discharging power, real-time charging and discharging capacity, and time-of-use power; and based on SOC and SOH information, the remaining charging and discharging capacity and charging and discharging power of each electrochemical energy storage power station are analyzed. The information collected by the hydrogen energy storage power station includes: available electrical or hydrogen energy, upper and lower limits of available electrical or hydrogen energy, efficiency of converting hydrogen energy into electrical energy and efficiency of converting electrical energy into hydrogen energy, upper and lower limits of hydrogen energy and electrical energy ramp-up, and upper and lower limits of heat-to-electricity ratio. The information collected by the cogeneration power plant includes: hydrogen input power, hydrogen-to-natural gas conversion efficiency, upper and lower limits of hydrogen input power and ramp rate, natural gas input power, natural gas-to-electricity and-heat conversion efficiency, upper and lower limits of natural gas input power and ramp power, and upper and lower limits of electro-thermal ratio. The information collected by the pumped storage power station includes: the water level stored in the upper reservoir, the upper and lower limit water levels, the power generation capacity of the generator, and the pumping capacity of the pump, and converts the water level of the upper reservoir into stored energy and upper and lower limit energy.

5. The distributed energy storage peak-shaving method based on the improved equilibrium optimization algorithm according to claim 1, characterized in that, The constraints on grid operation in step 3) include: the operating constraints of the distributed energy storage power station are shown in formulas (1) to (4), and the remaining operating constraints are as follows: I) Output constraints of renewable energy: In the formula: This is the upper limit of the output power of wind power generation. This represents the upper limit of the output power of photovoltaic power generation. II) Power balance constraints: P3(t)=P l (t)+P E (t)+P C (t)+P W (t)+P N,H (t)-P DG,w (t)-P DG,p (t)-P H,E (t)-P N,E (t)-P1(t)-P2(t) (13) In the formula: P l (t) represents the electrical load of the local power grid during time period t, P E (t) represents the electrical power input to all electrolyzers of the hydrogen energy storage power station during time period t, P C (t) represents the electrical power input to all electrochemical energy storage power stations during time period t, P W (t) represents the electrical power input to the pumped storage power station during time period t, P N,H (t) represents the electrical power input to all cogeneration plants during time period t, P H,E (t) represents the electrical power output of all hydrogen energy storage power stations during time period t, P N,E P1(t) represents the total power output of all cogeneration plants in time period t; P2(t) represents the thermal power generation of the local power grid in time period t; P3(t) represents the power generation of the remaining plants in the local power grid in time period t; P3(t) represents the amount of electricity purchased by the local power grid from the upstream power grid and the price thereon. If P3(t) is negative, then electricity is fed back to the upstream power grid. III) Thermal power balance constraint: P H,H (t)+P H,N (t)=P h (t) (14) In the formula: P H,H (t) represents the thermal power output of all hydrogen energy storage power stations during time period t, P H,N (t) represents the gross heat output of all cogeneration plants during time period t, P h (t) represents the heat load during time period t; IV) Natural Gas Balance Constraints P b (t)=P g (t)+P N (t)-P H,N (t) (15) In the formula: P b (t) represents the natural gas purchased during time period t, P g (t) represents the natural gas load during time period t, P N (t) represents the natural gas power consumed by all cogeneration plants during time period t, P H,N (t) represents the natural gas power output from the methane reactors of all cogeneration power plants during time period t; V) Hydrogen balance constraint P E,H (t)=P H (t)+P N,H (t) (16) In the formula: P E,H (t) represents the hydrogen power output from all the electrolyzers of the hydrogen energy storage power station during time period t, P H (t) represents the hydrogen power consumed by all hydrogen energy storage power stations during time period t, P N,H (t) represents the hydrogen power consumed by all cogeneration power plants during time period t.

6. The distributed energy storage peak-shaving method based on the improved equilibrium optimization algorithm according to claim 1, characterized in that, Step 3) establishes an optimization model that minimizes the total cost, specifically as follows: The total cost is calculated using electricity purchase cost, carbon emission cost, and wind and solar curtailment cost as the total cost, and the objective function is to minimize the total cost. The objective function is expressed as follows: F p =min(f p +f c +f DG,w +f DG,p ) (5) In the formula: F p f represents the total operating cost. p For the power grid's electricity purchase cost, f c For carbon emission costs, f DG,w For the cost of wind curtailment in wind power generation, f DG,p The cost of curtailment of photovoltaic power generation; The electricity purchase cost of the power grid is as follows: In the formula: P1 and M1 are the thermal power generation and grid connection price of the local power grid, respectively; P2 and M2 are the power generation and grid connection price of the other power plants in the local power grid, respectively; and P3 and M3 are the electricity and price purchased by the local power grid from the higher-level power grid, respectively. The carbon emission costs are as follows: Formula: E c The total carbon emissions are calculated from the carbon emissions from purchasing electricity from higher levels, E. 3c Carbon emissions E from power generation by other power plants in this grid 2c Total carbon emissions E from distributed energy storage t The composition of carbon dioxide absorbed by the methane reactor; a3, b3, and c3 are the carbon emission coefficients of coal-fired units in the upstream power grid, a2, b2, and c2 are the carbon emission coefficients of coal-fired units in the local power grid, and a1, b1, and c1 are the carbon emission coefficients of natural gas-consuming units, P t (t) represents the thermal energy P output by all cogeneration plants during time period t. N,H (t) and P output from all hydrogen fuel cell power plants H,H (t); ω is the calculated parameter for carbon dioxide absorption in the methane reactor, P H,N (t) represents the natural gas power output from the methane reactor; The actual total carbon emissions E are obtained from the above formula. c Based on the carbon emission credits of the local power grid, the carbon emission credits required to participate in the carbon emission trading market are calculated as follows: AND tt =And c -AND a (8) In the formula: E tt For the carbon emission credits actually used to participate in the carbon emission trading market, E a Carbon emission allowances allocated to this level of power grid; The carbon emission cost can be obtained from the above formula: f c =E tt ×f price (9) In the formula: f price The price per unit of carbon emission credits; The costs of curtailing wind and solar power are as follows: Wind curtailment cost f DG,w The details are as follows: Where: δ w The cost of wind curtailment penalty per unit; P DG,w (t) represents the wind curtailment power during time period t; Cost of abandoned light f DG,p The details are as follows: Where: δ p The cost of per unit of abandoned light penalty; P DG,p (t) represents the abandoned light power during time period t.

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