A power distribution network low-carbon transformation planning method considering multi-source uncertainty

Abstract

The application discloses a power distribution network low-carbon transformation planning method considering multi-source uncertainty, comprising the following steps: step 1, according to the parameters of the network to be planned and the parameters involved in the planning operation, a deterministic model of the low-carbon transformation planning model is constructed; step 2, an uncertainty model is constructed to obtain long-term uncertainty variable prediction values and short-term uncertainty variable prediction values; step 3, based on step 1 and step 2, a low-carbon transformation planning model is constructed; step 4, a multi-objective Harris hawk algorithm with nested generation column constraint reinforcement is used to solve the planning problem; and step 5, all planning solutions are output, and a preferred solution is selected according to a risk control target. The application can improve the planning results of power availability and cost optimization by considering the coordination of renewable energy generators and CCUS and multi-source uncertainty factors such as renewable energy output, user load, external energy price, carbon price and device unit deployment cost for the current power distribution network low-carbon transformation planning problem.

Description

Technical Field

[0001] This invention relates to robust planning methods for distribution networks, and particularly to a low-carbon transformation planning method for distribution networks that considers multi-source uncertainties. Background Technology

[0002] The accelerating pace of global warming has sparked widespread concern about controlling carbon emissions and implementing global low-carbon energy transition programs. Looking at the global carbon emission structure, the power sector accounts for a relatively high proportion of global carbon emissions, reaching as high as approximately 40%. Therefore, decarbonizing the power sector is crucial for achieving carbon neutrality. Against this backdrop, many countries have proposed their own transition projects to achieve low-carbon goals for the power sector.

[0003] Mainstream low-carbon transition solutions at the distribution network level can generally be divided into two main categories. One is to deploy distributed renewable energy to replace fossil fuel installations or dependence on external power sources, such as wind turbines (WT) and photovoltaics (PV). This approach reduces the proportion of carbon emissions from the distribution network by increasing the share of clean, original energy, as the electricity generated by these sources has virtually no marginal emissions. However, smoothing out the volatility of renewable energy output usually requires embedding other adjustment mechanisms or grid assets, including demand response (DR), energy storage (ES), multi-energy microgrids, and market mechanisms, to smooth the discrepancies between time-varying loads and renewable energy output.

[0004] Emerging carbon capture technologies offer another option. By retrofitting high-emission generators, carbon capture systems (CCS) can extract carbon dioxide from post-combustion exhaust gases and prevent its release into the atmosphere through storage. The high operating and deployment costs encourage the use of captured carbon dioxide (CCU) as a feedstock for other industrial applications to generate additional revenue, rather than simply storing it. The combination of CCS and CCU forms the basic concept of carbon capture, utilization, and storage (CCUS).

[0005] There is a potential contradiction between the two approaches mentioned above. Regional carbon emissions from energy systems generally consist of two parts: direct emissions from the combustion of fossil fuels and indirect emissions from imported external electricity. Only the former type of emissions can be captured by CCS. However, the increase in the proportion of local renewable energy generation will lead to a reduction in direct emissions. Sometimes, CCS may even face the extreme situation of having no carbon emissions to capture, which weakens its economic viability. Therefore, this contradiction should be considered in the low-carbon transformation of the distribution network.

[0006] Addressing uncertainty is another major challenge in low-carbon transition planning. These uncertainties typically stem from micro-level operational factors such as renewable energy and load fluctuations, equipment failures, and changes in external energy and carbon prices, as well as macro-level social and technological developments, such as energy policy and breakthroughs in emerging low-carbon technologies. These uncertainties involve diverse sources and different time scales. For example, retail electricity prices based on real-time pricing mechanisms and available solar / wind resources often change hourly. As for carbon prices, their purchase price fluctuates daily, and settlement is based on the fiscal year. Without loss of generality, it can be assumed that system operators can use arbitrage or bank excess emission rights to bring their annual unit carbon emission costs close to the average market price of the year, depending on the currently introduced carbon market mechanisms. Equipment costs are primarily determined by the technology or industry policies of the target deployment year. Therefore, whether these multi-source uncertainties are adequately considered will significantly impact the planning outcomes for power availability and cost optimization. Summary of the Invention

[0007] The purpose of this invention is to provide a low-carbon transformation planning method for distribution networks that considers multiple uncertainties. Addressing the current low-carbon transformation planning problems in distribution networks, this method considers the coordination of renewable energy generators and CCUS, as well as the multiple uncertainties of renewable energy output, user load, external energy prices, carbon prices, and equipment unit deployment costs. This approach can improve the planning results in terms of power availability and cost optimization.

[0008] To achieve the above objectives, the solution of the present invention is:

[0009] A method for planning the low-carbon transformation of distribution networks that considers multi-source uncertainties includes the following steps:

[0010] Step 1: Based on the parameters of the grid structure to be planned and the parameters involved in the planning and operation, construct a deterministic model of the low-carbon transformation planning model;

[0011] Step 2: Construct an uncertainty model and obtain predicted values ​​for long-term and short-term uncertainty variables;

[0012] Step 3: Based on Steps 1 and 2, construct a low-carbon transition planning model;

[0013] Step 4: Solve the planning problem using the nested generation column constraint-enhanced multi-objective Harris Eagle algorithm;

[0014] Step 5: Output all planning solutions and select the preferred solution based on the risk management objective.

[0015] In step 1 above, the goal of the low-carbon transition planning model is to minimize the investment cost of new equipment and the total operating cost of the energy supply system within the target period. Its constraints include capacity planning constraints and operational constraints.

[0016] The equipment subject to the above-mentioned operational constraints includes generators, new energy units, carbon capture equipment, electric gas generators, energy storage equipment, carbon storage equipment, and loads.

[0017] In step 2 above, a decision-making model based on envelope constraints is used to model long-term uncertainty, which is expressed as follows:

[0018]

[0019] in, For the uncertain set of equipment planning costs in an information gap decision-making model based on envelope constraints, α is the predicted value of equipment planning costs. eq For uncertainty margin, For the carbon quota price uncertainty set in the information gap decision-making model based on envelope constraints, π ER The price of carbon allowances. Predict prices for carbon allowances.

[0020] In step 2 above, an uncertainty budgeting model is used to model short-term uncertainty, which is expressed as follows:

[0021]

[0022] in, rs respectively s,t , The predicted value, where Δ is the preset deviation. For positive deviations of 0-1, Γ is a variable with negative deviation of 0-1. RS ,Γ L ,Γ E ,Γ G For uncertain budget parameters.

[0023] In step 3 above, the expression for the low-carbon transition planning model is:

[0024]

[0025] (1)-(24)

[0026] in, The user expects to plan the cost, RB sets the parameters for system uncertainty, C is the total cost, and C = C INV +σ year C OP , σ year To determine the number of years the system will operate.

[0027] The specific process of step 4 above is as follows:

[0028] Step 41: Obtain the predicted values ​​of long-term uncertainty variables;

[0029] Step 42: Based on the upper limit of the long-term uncertainty variable, solve the lower-level problem RO to obtain the first worst-case scenario; based on the lower limit of the long-term uncertainty variable, solve the lower-level problem RO to obtain the second worst-case scenario.

[0030] Step 43: Substitute the first worst-case scenario and the second worst-case scenario into the lower-level problem RO, and solve the planning model to obtain the Pareto solution.

[0031] Step 44, let re take the values ​​1, ..., nRep respectively, and perform the following operation:

[0032] Based on the re-th solution of the long-term uncertainty variable, solve the lower-level problem RO, determine whether the constraints are valid, and output the corresponding planning result if the constraints are valid; otherwise, discard the solution.

[0033] Step 45 yields all the planning solutions.

[0034] After adopting the above solution, the beneficial effects of the present invention are:

[0035] This invention comprehensively considers multiple uncertainties faced during the transformation of power distribution networks, such as equipment deployment costs, carbon allowance prices, and fluctuations in wind and solar resources. It classifies and models these uncertainties at different time scales, employing an uncertainty set model based on Information Gap Decision Technology (IGDT) to characterize medium- and long-term uncertainties such as equipment deployment costs and carbon allowance prices, and an uncertainty set model based on uncertain budgets to characterize short-term uncertainties such as wind speed, solar radiation, and load forecasting errors. This constructs a two-layer robust optimization model for low-carbon transformation planning of power distribution networks. To address the non-convex and nonlinear characteristics of the model, a nested generative column constraint-enhanced multi-objective Harris Eagle algorithm is designed to effectively solve the problem, laying the foundation for collaborative planning of low-carbon equipment in low-carbon power distribution networks. Attached Figure Description

[0036] Figure 1 This is a flowchart of the nested generation column constraint-enhanced multi-objective Harris Eagle algorithm in this invention;

[0037] Figure 2 It is a system network diagram;

[0038] Figure 3 It is a scene diagram;

[0039] Where (a) is the total load, (b) is the solar radiation, (c) is the wind speed, and (d) is the electricity price;

[0040] Figure 4 This is the solution result;

[0041] Figure 5This is a flowchart of the present invention. Detailed Implementation

[0042] like Figure 5 As shown, this invention provides a low-carbon transformation planning method for power distribution networks that considers multi-source uncertainties, comprising the following steps:

[0043] S101: Obtain the parameters of the network structure to be planned, including key parameters such as network topology, line impedance, line capacity, and voltage upper and lower limits;

[0044] S102: Obtain the parameters involved in the planning and operation, including the upper and lower limits of the planned capacity, the location of the planned equipment nodes, and the operation and maintenance costs;

[0045] S103: Obtain predicted values ​​for long-term uncertain variables, including predicted values ​​for the average carbon price in the operating year and equipment deployment costs;

[0046] S104: Obtain the predicted values ​​of short-term uncertain variables, including predicted values ​​of wind speed, photovoltaic power, load, upstream power source electricity purchase price, and natural gas sales price in specific operating scenarios;

[0047] S105: Construct the corresponding low-carbon transformation planning model based on equations (1)-(24);

[0048] S106: The proposed nested generative column constraint-enhanced multi-objective Harris Eagle algorithm is used to solve the corresponding planning problem;

[0049] S107: Output all planning solutions and select the preferred solution based on the risk management objective.

[0050] The technical solution and beneficial effects of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] 1. Model Construction

[0052] The main equipment considered in the low-carbon transformation planning model for the power distribution network includes fossil fuel generators, renewable energy generators, energy storage, carbon capture and storage (CCS), and power-to-gas (P2G). Generators provide energy to meet the needs of users in the industrial park and power electrical equipment such as CCS and P2G. CCS captures carbon dioxide emissions from fossil fuel generators. The captured carbon dioxide can be stored or fed into P2G as a feedstock to produce methane via the CO2 + 4H2 → CH4 + 2H2O process, generating additional revenue. The deterministic model for the low-carbon transformation planning of the power distribution network is introduced first.

[0053] 1.1 Objective Function

[0054] The goal of the planning model is to minimize the investment cost of new equipment and the total operating cost of the energy supply system within the target period. The total investment cost is expressed as follows:

[0055]

[0056] The expression for operating cost is:

[0057] C OP =C MT +C TR +C ENV +C CUR +C UN (2)

[0058] Where, ρ eq Cap eq The variables represent 0-1 values ​​for equipment planning and the planned capacity. `eq` represents the equipment name variable, including wind power (WT), photovoltaic (PV), energy storage (ES), carbon capture and storage (CCS), and power-to-gas (P2G). Among these, C... MT C TR C ENV C CUR C UN These are maintenance costs, transaction costs, environmental costs, curtailment penalties, and non-supply penalties, respectively, and their calculation formulas are as follows:

[0059]

[0060] Among them, c GEN c GEN,on c GEN,off c WT c PV c ES c CCS c P2G These are the maintenance costs per unit of generator power, single start-up cost, single shutdown cost, wind power maintenance cost per unit of generator power, photovoltaic power maintenance cost per unit of photovoltaic power, CCS maintenance cost per unit of power, and P2G maintenance cost per unit of power. These represent the generator power output, start-up and shutdown 0-1 indicator variables, wind power output, photovoltaic power output, total energy storage charging and discharging power, CCS input power, and P2G input power at time t in scenario s. fuel , κ buy These are generator fuel price, electricity purchase price, and carbon dioxide procurement cost; Consume fuel for the generator. For the power purchase capacity, Producing fuel for P2G This refers to the amount of carbon dioxide purchased. π ER For carbon quota prices, The carbon emission costs of the distribution network that should be included in carbon accounting; κ seq Cost per unit of carbon dioxide sequestration This refers to the amount of carbon sequestration. CUR As a penalty for abandoning electricity by the unit, This refers to the amount of electricity wasted from solar and wind power. UN The unit failed to provide punishment. For node i, the unsupplied load; These are the sets of scenarios and the sets of scheduling times, respectively. It is a set of distribution network nodes.

[0061] 1.2 Constraints

[0062] 1.2.1 Planning Constraints

[0063] Capacity planning should meet certain upper and lower limits, such as

[0064]

[0065] In the formula, u eq For equipment planning, 0-1 variables, and Cap eq These represent the upper and lower limits of the planned equipment capacity.

[0066] 1.2.2 Operational Constraints

[0067] The operational constraints considered in the planning include equipment operation and energy conversion models, user load models, and network and balance models.

[0068] (1) Generator

[0069] Generator constraints include:

[0070]

[0071] in, The generator set start / stop status is a 0-1 variable. P GEN , These represent the upper and lower limits of the generator's active power output. Q GEN , These represent the upper and lower limits of the generator's reactive power output. For carbon emissions from generators, m C Here, represents the relative molecular mass of carbon dioxide molecules and carbon atoms, and OF represents the oxidation rate. To consume fuel, T is the upper limit of climbing power. GEN,on T GEN,off These are the start and stop times, respectively.

[0072] (2) New energy units

[0073] Constraints of new energy units

[0074]

[0075] in, These represent the upper limit of output of the new energy generating unit, the actual output, and the power curtailment, respectively; w and r represent wind speed and solar irradiance, respectively. This is the adjustable range coefficient for reactive power output, and its value, according to the manual, is cosarctan 0.95. To exert effort for no reason; Cap rg Plan capacity for distributed clean energy.

[0076] (3) CCS

[0077]

[0078] in, The CCS start / stop status is a 0-1 variable. η is the base power for CCS startup and operation. CCS For carbon capture efficiency. The amount of carbon dioxide captured; M is a large number, usually 1e6.

[0079] (4)P2G

[0080]

[0081] in, The P2G start / stop status is a 0-1 variable. η is the base power for P2G startup and operation. P2G For P2G efficiency, H G It is a low-calorific-value natural gas. The amount of natural gas produced for P2G. For P2G to consume carbon dioxide, υ G This refers to the density of natural gas.

[0082] (5)ES

[0083]

[0084] in, For the current energy storage capacity, These represent the energy storage charging and discharging power, Δt is a scheduling time, and η is the energy storage charging and discharging power. ES,ch η ES,dis For charging and discharging efficiency, k represents the charge / discharge state 0-1 variable. ES,φ This is the adjustable range coefficient for reactive power output of energy storage, and its value, according to the manual, is tan arccos0.95. It provides reactive power for energy storage.

[0085] (6) Carbon Storage (CS)

[0086]

[0087] in, It is the carbon storage capacity, η CS For the efficiency of carbon dioxide filling and releasing. This is the upper limit of carbon storage capacity. These represent the amounts of carbon dioxide added and released, respectively. The states of carbon dioxide filling and releasing are 0-1 variables. These are the upper and lower limits for carbon storage and release.

[0088] (7) Load

[0089] For nodes Its load model is

[0090]

[0091] in, For user load demand, For the actual load power dispatched by the user, For user load reactive power, This represents the unsupplied load at node i.

[0092] (8) Alternating linear power flow

[0093]

[0094] Let ε be the mapping of devices to nodes in the network, and let ε be the set of branches in the network. It is through the active and reactive power of the line, VS i,s,t R is the square of the per-unit value of the node voltage. ij X ij These are the line resistance and reactance parameters. This is the upper limit of the line's transmission capacity. The upper limit for the amount of electricity purchased; P i,s,t Q i,s,t These represent the active load and reactive load of the nodes, respectively. 1,s,t This is the square of the per-unit value of the relaxation node voltage. VS These are the upper and lower limits of the square of the per-unit voltage value, respectively. Power is purchased externally.

[0095] (9) Carbon balance constraint

[0096]

[0097] in, Net carbon dioxide emissions generated by the power distribution network. For the purchased carbon dioxide, For the sequestered carbon dioxide Indirect carbon emissions introduced from purchased electricity; δ up The carbon emission factor of the upstream power source. The carbon emissions of the system that should be included in the carbon quota assessment.

[0098] 1.3 Uncertainty Analysis

[0099] 1.3.1 Long-term uncertainty modeling

[0100] Carbon emission allowance prices and the unit deployment costs of new emission reduction equipment, including Wt, PV, ES, CCS, and P2G, are long-term uncertainties. Because they lack historical data or are susceptible to social and economic exogenous factors, it is difficult to generate scenarios that fluctuate on an intraday timescale, similar to renewable energy fluctuations and energy prices. This embodiment proposes an envelope-constrained information gap decision model to model this type of uncertainty. Its specific form is as follows:

[0101]

[0102] in, For the uncertain set of equipment planning costs in an information gap decision-making model based on envelope constraints, α is the predicted value of equipment planning costs. eq For uncertainty margin, For the carbon quota price uncertainty set in the information gap decision-making model based on envelope constraints, π ER The price of carbon allowances. Predict prices for carbon allowances.

[0103] 1.3.2 Modeling Short-Term Uncertainty

[0104] Short-term uncertainties include available renewable resources, energy prices, and load demand. These parameters are characterized by: (1) frequent intraday or interday fluctuations; (2) a wealth of historical data that can be analyzed through data mining; and (3) primarily influenced by seasonal factors, with limited impact from economic, policy, and other factors. Therefore, compared to long-term uncertainties, a more sophisticated approach is needed to model such uncertainties to achieve a balance between optimality and conservatism, rather than simply specifying upper and lower limits. This invention employs an uncertainty budgeting model to model short-term uncertainties:

[0105]

[0106] in, rs respectively s,t , The predicted value, where Δ is the preset deviation. For positive deviations of 0-1, Γ is a variable with negative deviation of 0-1. RS ,Γ L ,Γ E ,Γ G For uncertain budget parameters.

[0107] 2. Two-level robust programming model

[0108]

[0109] in The user expects to plan the cost, RB sets the parameters for system uncertainty, C is the total cost, and C = C INV +σ year C OP , σ year To determine the number of years the system will operate.

[0110] 3 Solution Strategies

[0111] 3.1 Nested Column Constraint Generation Algorithm

[0112] This section describes the main steps of the Nested Column & Constraint Generation Algorithm (Nested C&CG). The lower-level problem RO in (24) can be written in the following general form.

[0113]

[0114] Where c, d, and g are the cost coefficients of the decision variables, y is the first-stage optimization variable, x and z are the second-stage optimization variables, and Ay≤b is the first-stage constraint. To consider the range of values ​​for the second-stage optimization variables, taking into account uncertainty, let... The main problem can then be expressed as:

[0115]

[0116] Assume y * Given that, the subproblem can be written in the following form

[0117]

[0118] Assuming feasible region It can be represented as The submaster problem (InMP) and the subslave problem (InSP) can then be expressed as follows:

[0119]

[0120] The subproblems can be solved by embedding the KKT problem into the master problem. The pseudocode for this method is as follows:

[0121] Algorithm 1: Nested Column Constraint Generation Algorithm Input: Cost parameters ρ, π ER

[0122] Output: Planning result u, Cap

[0123] Initialization: OutUB←+∞, OutLB←-∞, ∈←+∞, m←0;

[0124] While do

[0125] m←m+1;

[0126] Solve for MP to obtain y (m),* ;

[0127]

[0128] InUB←+∞, InLB←-∞, ∈′←+∞, n←0;

[0129] While do

[0130] n←n+1;

[0131] Derive the dual multiplier λ of InSP (n) Add the KKT conditions to InMP;

[0132] Solve for InMP to obtain γ (n),* ;

[0133] InUB←θ (n),* ;

[0134] Solve for InSP to obtain (x (n),* ,z (n),* );

[0135] InLB←max(InLB,dx (n),* +gz (n),* );

[0136]

[0137] end

[0138] OutUB←min(OutUB,cy (n),* +InLB);

[0139] Establish variable (x) (m+1) ,z (m+1 ))

[0140] Add the following constraints to MP:

[0141] η≥dx (m+1) +gz (m+1) Ex (m+1) +Gz (m+1) ≤f-Rγ (m+1),* -Dy

[0142]

[0143] end

[0144] 3.2 Multi-objective Harris Eagle Optimization

[0145] The main steps of the Harris Hawk Optimization (HHO) algorithm consist of two phases: the exploration phase and the development phase.

[0146] (1) Exploration: The exploration phase simulates the behavior of eagles when detecting prey. Their positions indicate the target solution for the (τ+1)th iteration, and their positions are randomly generated by the following formula.

[0147]

[0148] Among them, X rabbit (τ) is the rabbit's position, X rand (τ) is the randomly selected position of the eagle, X m (τ) is the average position of the current population. UB and LB define the upper and lower limits of the variables to be solved, which are the optimization variables of the upper-level problem in equation (24). r1 to r4 are random parameters between (0,1).

[0149] (2) The shift from exploration to utilization:

[0150] By simulating real hunting scenarios, the eagle's behavior shifts from exploration to exploitation as the prey's escape energy decreases. The prey's remaining escape energy EN is modeled as...

[0151]

[0152] EN0 represents the randomly generated prey energy level.

[0153] (3) Development Phase: In this phase, the eagle surrounds its prey and adjusts its hunting behavior according to the prey's condition. During the development phase, the eagle will formulate four strategies, and the specific strategy adopted depends on the escape energy (EN) and the prey's chances of successfully escaping.

[0154] Strategy 1: Soft Encirclement

[0155]

[0156] Where J = 2(1-r5) is the length of the rabbit's random jump, and r5 is a random parameter between (0,1).

[0157] Strategy 2: Hard Encirclement

[0158] X(τ+1)=X rabbit (τ)-EN|ΔX(τ)| (32)

[0159] Strategy 3: Soft encirclement and gradual rapid dive

[0160] At this stage, the prey has a chance to escape the encirclement, so the flock of eagles needs to develop a more intelligent encirclement route before capturing the prey. This strategy considers two types of next actions, defined as...

[0161]

[0162] D is the dimension of the problem, and S is a 1×D random vector.

[0163]

[0164] Where β = 1.5, μ and σ are random variables in the range (0,1), therefore, in the next iteration, the eagle's position is...

[0165]

[0166] F(·) is the value of the adaptation function, which is the value of the objective function of the upper-level model in equation (24).

[0167] Strategy 4: Hard encirclement and gradual rapid dive

[0168] Similar to strategy 3, two motion strategies are defined.

[0169]

[0170] The judgment criteria are the same as those for strategy 3.

[0171] The pseudocode for a multi-target HHO is as follows:

[0172] Algorithm 2: Multi-objective Harris Eagle Optimization Algorithm

[0173] Input: Population size nPop, storage size nRep, maximum iteration MaxIt, upper and lower bounds of the optimization variables in (24).

[0174] Output: Pareto solution set X rep

[0175] Initialization: it←0, Generate the initial population Xi ,i=1,…,nPop

[0176] Calculate the fitness of the initial population. The fitness function is the objective function of the upper-level model in (24).

[0177] Let X rabbit Location of the prey

[0178] Determine if it is a Pareto solution and put it into X. rep middle

[0179] for it=1:MaxIt do

[0180]

[0181] for i = 1:nPop do

[0182] From X rep Choose one solution as the leader.

[0183] EN0←2RAND-1,r←RAND

[0184] EN←EN0×EN1

[0185] If EN≥1, proceed to the exploration phase.

[0186] if EN < 1

[0187] If EN≥0.5 &r≥0.5, execute strategy 1.

[0188] If EN < 0.5 & r ≥ 0.5, execute strategy 2 end

[0189] If EN≥0.5&r<0.5, execute strategy 3 end

[0190] If EN < 0.5 &r < 0.5, execute strategy 4 end

[0191] end

[0192] Mutations:

[0193] Mu←RAND

[0194] if Mu < pm, the current mutation X rabbit end

[0195] If Mu ≥ pm, use the previous mutation to update X. rabbit end

[0196] Add the non-dominated solution to X. rep

[0197] Determine the current X rep Does the solution contain dominant solutions and only retain non-dominated solutions?

[0198] Check X rep Is it full?

[0199] end

[0200] 3.3 Nested Generation Column Constraint Enhanced Multi-Objective Harris Hawk Algorithm

[0201] This invention employs the Nested Column & Constraint Enhanced Multi-objective Haris Hawk Optimization (Nested C & CGenhanced MOHHO) algorithm to solve a two-layer multi-objective mixed integer optimization problem such as (24). Its basic process is as follows: Figure 1 As shown.

[0202] The following is an application example of the present invention.

[0203] 3.1 Operating Parameters

[0204] This section simulates a ten-year planning and operational period. For example... Figure 2 As shown, the unit planning costs for PV, WT, ES, CCS, and P2G are RMB 6.10, 8.78, 1.77, 0.55, and 10.75 million per MW or MWh, respectively. We assume planning begins in 2026. The carbon credit price is RMB 116.18 / tCO2. The annual operation includes four scenarios to describe the potential possibilities, with the electricity purchase price and natural gas sales price used in each scenario as follows: Figure 3 As shown. The carbon emission factor of the upstream power grid is fixed at 0.4872. The short-term uncertainty offset is set to 10%, and the long-term uncertainty offset variable ranges from [0, 0.5]. The storage size is 10, the population size is 10, and the maximum number of iterations is 20. RB is set to 0.2.

[0205] Table 1 Operating Parameters

[0206]

[0207] Table 2 Planning Parameters

[0208]

[0209] 3.2 Solution Results

[0210] Example: Solve 10 solutions, such as Figure 4As shown, the planning values ​​corresponding to Sol.1 are PV: 0.289, WT: 1.005, ES: 5.982, CCS: 0.859, P2G: 0.100, and the total cost is RMB 130.439 million.

[0211] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0212] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0214] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0215] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0216] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for planning the low-carbon transformation of distribution networks considering multi-source uncertainties, characterized in that... Includes the following steps: Step 1: Based on the parameters of the grid structure to be planned and the parameters involved in the planning and operation, construct a deterministic model of the low-carbon transformation planning model; Step 2: Construct an uncertainty model and obtain predicted values ​​for long-term and short-term uncertainty variables; In step 2, a long-term uncertainty model is used to model the information gap decision-making model based on envelope constraints. In step 2, an uncertainty budget model is used to model short-term uncertainty. Step 3: Based on Steps 1 and 2, construct a low-carbon transition planning model; Step 4: Solve the planning problem using the nested generation column constraint-enhanced multi-objective Harris Eagle algorithm; Step 5: Output all planning solutions and select the preferred solution based on the risk management objective.

2. The method as described in claim 1, characterized in that: In step 1, the goal of the low-carbon transition planning model is to minimize the investment cost of new equipment and the total operating cost of the energy supply system within the target period. Its constraints include capacity planning constraints and operational constraints.

3. The method as described in claim 2, characterized in that: The equipment subject to the operational constraints includes generators, new energy units, carbon capture equipment, electric gas generators, energy storage equipment, carbon storage equipment, and loads.

Images