Two-stage distribution robust optimization scheduling method of multi-energy complementary system

Abstract

The invention discloses a two-stage distribution robust optimization scheduling method of a multi-energy complementary system, and belongs to the technical field of power system optimization scheduling. According to the method, for the problem of photovoltaic output and electrical load uncertainty, thermal inertia characteristics and energy storage capacity of a heat supply system and a cold supply system are comprehensively considered, an uncertainty set based on the Wasserstein distance is constructed, then a two-stage distribution robust optimization model with a min-max-min structure is established, and the photovoltaic output and the electrical load are optimized. And carrying out joint solution on the two-stage distribution robust optimization model by adopting a model linearization reconstruction and column and constraint generation algorithm, so as to obtain an optimal scheduling scheme of the multi-energy complementary system under the most unfavorable probability distribution of the source load. The method can effectively improve the robustness and economy of a scheduling strategy, and is suitable for system operation optimization under the background of deep access of distributed renewable energy sources.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system optimal dispatching, and particularly relates to a two-stage distribution robust optimization dispatching method for a multi-energy complementary system. BACKGROUND

[0002] With the wide access of renewable energy and the continuous optimization of energy structure, modern power systems are facing challenges brought by source-side and load-side uncertainties. Traditional centralized dispatching methods are difficult to adapt to complex operating environments such as large-scale access of distributed energy, increasing volatility of power load, and enhanced coupling of multi-energy systems.

[0003] As a bridge connecting various distributed resources and grid management sides, the complementary system integrates distributed power generation, energy storage systems, and controllable loads, etc. resources, can aggregate a large number of adjustable resources, and actively participate in grid dispatching and energy market transactions such as power market, realizes the optimization of energy scheduling and supply and demand balance, and improves the energy utilization efficiency and the stability of power grid operation. However, the existing complementary system faces many challenges in the process of operation. First, the distributed energy in the multi-energy complementary system has randomness and volatility, which makes its output difficult to accurately predict, increasing the uncertainty of dispatching. Second, the complementary system needs to coordinate multiple energy forms and different interest subjects, and there are differences in the interests of each subject. How to balance the interests of each party and realize the optimal operation of the whole system is a problem to be solved. In addition, the traditional dispatching method often ignores the dynamics and randomness of the energy market, and cannot adapt to the complex market environment and variable load demand. SUMMARY

[0004] To solve the problems in the prior art, the application provides a two-stage distribution robust optimization dispatching method for a multi-energy complementary system.

[0005] The technical scheme of the application is as follows:

[0006] The application discloses a two-stage distribution robust optimization dispatching method for a multi-energy complementary system, comprising the following steps:

[0007] S1, obtaining a multi-energy complementary system comprising distributed photovoltaic, energy storage unit, gas turbine, cold and heat system, and adjustable demand response resource;

[0008] S2, based on the uncertainty of photovoltaic output and electrical load in the multi-energy complementary system, constructing an uncertainty set by using Wasserstein distance, and establishing a two-stage distribution robust optimization model with min-max-min structure;

[0009] S3, based on the two-stage distribution robust optimization model, a linearization technique is used to obtain a linearization model, and finally the linearization model is solved by the column and constraint generation algorithm to obtain the optimal scheduling scheme of the multi-energy complementary system.

[0010] Compared with the prior art, the beneficial effects of the present application are:

[0011] The present application proposes an innovative two-stage distribution robust optimization scheduling method for multi-energy complementary systems, effectively addressing the challenges brought by distributed photovoltaic output and load power uncertainty, significantly improving the economy and low carbon nature of multi-energy complementary systems, while ensuring the robustness of operation. The method constructs a distribution robust optimization model based on the Wasserstein distance, considering a variety of possible probability distribution uncertainties, avoiding overly conservative scheduling strategies compared to traditional robust optimization methods, and overcoming the defect of being overly optimistic compared to stochastic optimization, thereby achieving a more optimal operation strategy in an uncertain environment. The model integrates various flexible resources such as distributed energy storage, demand response loads, and combines the thermal inertia characteristics of heating / cooling systems, fully utilizing energy storage capabilities, and optimizing the output of various resources to effectively reduce peak load pressure and reduce operating costs and carbon emissions. The present application not only improves the adaptability of multi-energy complementary systems to new energy consumption and multi-energy load management, but also provides a scientific basis and technical support for the low-carbon and efficient operation of multi-energy complementary systems in the electricity market environment, which is of great significance for promoting energy transformation and achieving carbon neutralization targets. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 Flow chart of the two-stage distribution robust optimization scheduling method for multi-energy complementary systems;

[0013] Figure 2 Influence of PMV evaluation index on heating system;

[0014] Figure 3 Electricity scheduling scheme when the operating cost is lowest;

[0015] Figure 4 Thermal energy scheduling scheme when the operating cost is lowest;

[0016] Figure 5 Natural gas scheduling scheme when the operating cost is lowest;

[0017] Figure 6 Cooling power scheduling scheme when the operating cost is lowest;

[0018] Figure 7 Electricity scheduling scheme when the carbon emission is lowest;

[0019] Figure 8 Thermal energy scheduling scheme when the carbon emission is lowest;

[0020] Figure 9 a cooling power dispatching scheme with the lowest carbon emission;

[0021] Figure 10 a cooling power dispatching scheme with the lowest carbon emission. DETAILED DESCRIPTION

[0022] The present application will be further described and illustrated in conjunction with the specific embodiments. The embodiments are only exemplary and do not define the scope of the disclosure. The technical features of various embodiments of the present application can be combined without conflict, as appropriate.

[0023] The present application relates to a two-stage distribution robust optimization dispatching method of a multi-energy complementary system. In view of the uncertainty of photovoltaic output and various load powers, the thermal inertia characteristics and energy storage capacity of heating and cooling systems are comprehensively considered, an uncertainty probability distribution set based on Wasserstein distance is constructed, and a two-stage min-max-min structure dispatching model with economic cost or carbon emission as the optimization target is established through a distribution robust optimization algorithm. In the model solving process, a model linearization reconstruction and column and constraint generation algorithm are jointly used for solving, so that the optimal dispatching scheme of the multi-energy complementary system under the most unfavorable probability distribution of the source and load is obtained. The method can effectively improve the robustness and economy of the dispatching strategy, and is suitable for the operation optimization of a multi-energy complementary system in the context of deep access of distributed renewable energy.

[0024] REFERENCE Figure 1 The two-stage distribution robust optimization dispatching method of the multi-energy complementary system of the present application, around the efficient dispatching problem of the multi-energy complementary system, constructs a collaborative optimization system containing various energy forms such as electricity, heat, cold and gas, and integrates distributed photovoltaic, energy storage devices, demand response and gas turbine and other flexible resources. On the basis of considering the thermal inertia of the system and the comfort of the users, the PMV index, the ARMA model and the ETP model are used to finely model the thermal environment and the energy supply system, and the two-stage distribution robust optimization (WDRO) method based on the Wasserstein distance is introduced to cope with the uncertainty of photovoltaic output and load power. The dispatching model describes the minimum economic cost and carbon emission under the most unfavorable scenario through the "min-max-min" structure, and combines the column and constraint generation algorithm for linear reconstruction and efficient iterative solving, to ensure that the system still has economy and low carbon under the most unfavorable distribution. The technical path effectively realizes the aggregation response and dynamic adjustment of flexible resources, and provides technical support for the robust dispatching of multi-energy systems in complex uncertain environments.

[0025] As Figure 1As shown, the two-stage distribution robust optimization scheduling method of the multi-energy complementary system of the application comprises the following steps:

[0026] S1, acquire a multi-energy complementary system comprising a distributed photovoltaic, an energy storage unit, a gas turbine, a cold and heat system and an adjustable demand response resource; wherein the adjustable demand response resource comprises a P2G device; the cold and heat system comprises an electric refrigerator, an absorption refrigerator, a gas boiler and a waste heat boiler;

[0027] S2, based on the uncertainty of photovoltaic output and electrical load in the multi-energy complementary system, an uncertainty set is constructed by using Wasserstein distance, and a two-stage distribution robust optimization model with a min-max-min structure is established;

[0028] S3, based on the two-stage distribution robust optimization model, a linearization technique is used to obtain a linearization model, and finally the linearization model is solved by a column and constraint generation algorithm to obtain an optimal scheduling scheme of the multi-energy complementary system under the most unfavorable probability distribution of source and load.

[0029] Specifically, the uncertainty set in S2 is :

[0030]

[0031] In the formula, 1-norm distance between the final probability distribution and the original probability distribution is represented; 1-norm distance between the uncertainty variables is represented; joint probability distribution of , is represented; is the initial probability distribution value of the uncertainty factor sample, , represents the original photovoltaic probability distribution, represents the original electrical load probability distribution; is the adverse probability distribution value of the uncertainty factor, , represents the final photovoltaic probability distribution, represents the final electrical load probability distribution; is the 1-Wasserstein sphere radius; is the ∞-Wasserstein sphere radius; is the confidence of 1-Wasserstein distance; is the confidence of ∞-Wasserstein distance; , are respectively random variables subject to , ; and Then the support set is denotes the probability that the 1-Wasserstein distance is less than a specified value; denotes the infinite norm distance between the final probability distribution and the original probability distribution; denotes the infinite norm distance between the final probability distribution and the original probability distribution; denotes the probability that the infinite-Wasserstein distance is less than a specified value.

[0032] In S2, the two-stage distribution robust optimization model needs to model the flexibility adjustment resources of the multi-energy complementary system, including distributed photovoltaic, energy storage units, gas turbines, cold and heat loads, and adjustable demand response resources, etc. The user comfort is measured and constrained by the PMV index (Predicted Mean Vote), the heating system is modeled by the ARMA (Autoregressive Moving Average) model for its dynamic response, and the cooling system is modeled by the ETP (Equivalent Thermal Parameter) model for its thermal buffering characteristics.

[0033] Firstly, the modeling of distributed photovoltaic units is introduced. Compared with traditional centralized photovoltaic systems, distributed photovoltaic generation has the advantages of decentralization, modularity, and scalability, which can greatly improve the effectiveness and flexibility of energy utilization. Therefore, multi-energy complementary system operators can connect various distributed photovoltaic units through advanced communication and control means, collect data in real time, and process and adjust them. The power output calculation model of distributed photovoltaic units is as follows:

[0034]

[0035] In the formula, denotes the actual photovoltaic output of the distributed photovoltaic unit at time t; denotes the conversion efficiency of the distributed photovoltaic unit; S denotes the effective area of the photovoltaic component in the distributed photovoltaic unit; I denotes the solar radiation intensity; denotes the ambient temperature at time t. The distributed photovoltaic output constraint is as follows:

[0036]

[0037] In the formula, denotes the minimum value of the actual photovoltaic output of the distributed photovoltaic unit at time t; denotes the maximum value of the actual photovoltaic output of the distributed photovoltaic unit at time t.

[0038] ​The distributed energy storage device (energy storage unit) mainly refers to a small energy storage device distributed at a user side and a power grid side, and can store energy through a battery, a super capacitor, a flywheel and other technical means. The present application takes an electrochemical energy storage device as a research object, and a running process thereof mainly depends on a state of charge of a multi-energy complementary system. In order to ensure safe and stable operation, the state of charge of the distributed energy storage device needs to meet certain upper and lower limits, and initial and final states of charge in a same dispatching period need to be consistent, so that a charging and discharging process thereof can be expressed as:

[0039]

[0040]

[0041] In the formula, respectively represent charging and discharging power of the energy storage device at t time; is a capacity of the energy storage device at a starting moment; and are maximum and minimum residual capacities of the energy storage device; is a dispatching period; represents a charging and discharging efficiency.

[0042] Considering that a change in a system power consumption plan can affect user comfort, a multi-energy complementary system operator needs to give appropriate compensation to the user, and a dispatching cost of the multi-energy complementary system in a t period can be expressed as:

[0043]

[0044]

[0045] In the formula, represents a demand response cost of the multi-energy complementary system; represents a demand response coefficient; represents upward power of the demand response; represents downward power of the demand response; represents original demand response load power; represents load power after the demand response.

[0046] A gas turbine can be rapidly started and stopped, and has an advantage of flexibly adjusting output power. The present application selects a gas turbine taking natural gas as a main fuel, provides power support for the multi-energy complementary system through consumption of the natural gas, can effectively undertake a peak load shifting task, and enhances stability and flexibility of the multi-energy complementary system. In a high load peak period of a power grid, the gas turbine can flexibly adjust output power, respond to a peak shaving instruction of a superior power grid, and replace part of user load for adjustment, so as to guarantee user power consumption experience. In the present application, power generation of the gas turbine and a constraint thereof are as follows:

[0047]

[0048]

[0049] In the formula, Indicates the output power of the gas turbine; This indicates the amount of natural gas consumed by the gas turbine; Indicates the efficiency of the gas turbine; and This indicates the upper and lower limits of the actual output of the gas turbine; State variables used to indicate the start and stop of the gas turbine; and This indicates the upper and lower limits of the gradeability of the gas turbine. Indicates the operating and shutdown time of the gas turbine; This indicates the minimum operating time and minimum downtime of the gas turbine.

[0050] The PMV (Potential Thermal Value) index comprehensively considers six factors: clothing thermal resistance, human activity level, air temperature, mean radiant temperature, air velocity, and air humidity. It aims to assess the degree of deviation from the human body's thermal balance. This index uses a 7-level scale system to quantify the human body's thermal sensation. The calculation formula is as follows:

[0051]

[0052] In the formula, M is the human body's energy metabolism rate, and W is the mechanical power exerted by the human body. It is the ratio of the area of ​​the body covered by clothing to the area not covered. The surface heat transfer coefficient; The water vapor pressure of the environment in which the human body is located; Indicates the water temperature at the location; The temperature of the air in contact with the human body; These are the outer layer temperature of clothing and the average radiant temperature of the environment in which the human body is located, respectively.

[0053] To address the issue of insufficient peak-shaving capacity due to thermoelectric coupling in heating units during winter heating seasons, this problem is solved by utilizing the thermal inertia of the heating system, which is composed of related resources within a multi-energy complementary system. In heating systems, temperature regulation is primarily achieved through two methods: qualitative and quantitative regulation. The thermal inertia of a heating system based on qualitative regulation is generally described using the ARMA model, as shown below:

[0054]

[0055]

[0056] In the formula, is the return water temperature of the heating pipeline at time t, is the supply water temperature of the heating pipeline at time t-j; is the outdoor temperature at time t-j; is the indoor temperature of the building at time t-j; J is the thermal inertia of the heating system; represents the index coefficient; 、 、 、 、 、 are all used to describe the thermal inertia of the heating system.

[0057] In the multi-energy complementary system, the cooling system also has the ability of "cold storage". Therefore, the equivalent thermal parameter model is used to describe the thermal response process of the cooling system:

[0058]

[0059] In the formula, are the indoor temperature and the outdoor temperature, respectively; R and C are the equivalent thermal resistance and the equivalent thermal capacity of the cold building, respectively, and the greater the equivalent thermal resistance, the stronger the "cold storage" characteristics of the system and the slower the temperature change response; is the total refrigeration power at time t; represents the dispatching time interval.

[0060] In order to ensure the optimal scheduling strategy of the multi-energy complementary system, the operator needs to consider the uncertainty of photovoltaic and electrical load. By constructing a two-stage distributionally robust optimization (WDRO) model, taking the economic cost optimization as an example, the mathematical model is as follows:

[0061]

[0062] In the formula, are the day-ahead scheduling strategy and the optimal scheduling scheme, respectively; is the adverse probability distribution of uncertain factors, is the uncertain set; represents the dispatching time; is the charging and discharging cost of the multi-energy complementary system, is the power interaction cost of the upper-level power grid, is the demand response cost, is the gas turbine operation cost, is the gas purchase cost.

[0063] In this model, the outer structure is the core problem of day-ahead scheduling, which integrates the most adverse scenario probability distribution value generated by the inner sub-problem and the optimal dispatching strategy x is determined based on the distribution; the inner sub-problem deals with the regulation challenge due to the uncertainty of photovoltaic output and load power, and the main goal is to minimize the operation cost of the multi-energy complementary system under the constraints of the system The most adverse scenario probability distribution and the optimal dispatching scheme are identified .

[0064] The operation cost is shown as follows:

[0065]

[0066] wherein, represents the operation cost; T represents the dispatching time; represents the converted unit charging and discharging cost; represents the purchased power; represents the sold power; respectively represent the unit price of the multi-energy complementary system for purchasing and selling power to the upper-level power grid; represents the unit power generation cost of the gas turbine; represents the natural gas supply amount at t; represents the unit energy cost of the natural gas.

[0067] The constraint conditions of the two-stage distribution robust optimization model include the output constraints of the P2G device, the waste heat boiler, the gas boiler, the absorption refrigeration machine and the electric refrigeration machine, and the energy balance constraints of the power grid, the natural gas network, the heat network and the indoor energy of the cold building in the multi-energy complementary system.

[0068] The output constraint of the P2G device in the multi-energy complementary system is:

[0069]

[0070]

[0071] wherein, represents the natural gas consumed by the P2G; represents the electric power generated by the P2G; represents the energy conversion efficiency of the P2G device, , respectively represent the minimum and maximum output values of the P2G device.

[0072] The output constraints of the waste heat boiler and the gas boiler are:

[0073]

[0074] wherein, represents the heat power generated by the waste heat boiler; represents the gas power consumed by the gas turbine; ​respectively the upper and lower limits of the heat power output by the waste heat boiler; denotes the efficiency of the gas turbine; , denote respectively the thermal efficiency of the waste heat boiler and the gas boiler; denotes the heat power generated by the gas boiler; , respectively the upper and lower limits of the heat power output by the gas boiler.

[0075] The power constraints of the absorption chiller and the electric chiller are as follows:

[0076]

[0077] wherein, denotes the cold power output by the absorption chiller; denotes the heat power consumed by the absorption chiller; and denote respectively the energy conversion efficiency of the absorption chiller and the electric chiller; and respectively the upper and lower limits of the power output by the absorption chiller; denotes the electric power consumed by the electric chiller; denotes the cold power generated by the electric chiller; , respectively the upper and lower limits of the power output by the electric chiller.

[0078] The energy balance of the electric grid within the multi-energy complementary system is as follows:

[0079]

[0080] wherein, denotes the load power; denotes the electric power generated by the electric chiller; denotes the binary coefficient of purchased electricity; denotes the maximum value of the exchanged power between the multi-energy complementary system and the large grid.

[0081] The energy balance of the natural gas network within the multi-energy complementary system is as follows:

[0082]

[0083] wherein, denotes the gas load; denotes the gas power consumed by the gas turbine; denotes the gas power consumed by the gas boiler.

[0084] The energy balance of the heat grid is as follows:

[0085]

[0086] wherein, represents the heat load; represents the heat power consumed by the absorption refrigeration machine; represents the relationship coefficient between the heat supply and the supply and return water temperatures, represents the maximum value of the supply water temperature, represents the upper limit of the fluctuation of the PMV index.

[0087] The indoor energy balance of the cold-adopting building is constrained as follows:

[0088]

[0089]

[0090] wherein, , respectively represent the upper and lower limits of the indoor temperature of the building.

[0091] The specific steps of S3 are as follows:

[0092] Since the model of the multi-energy complementary system is a two-stage WDRO, it has strong nonlinearity and non-convexity, and the optimal solution cannot be directly solved, so the model is redefined in the form of a matrix:

[0093]

[0094] wherein, represents the first-stage cost coefficient matrix; represents the second-stage cost coefficient matrix; represents a function containing uncertain variables; represents the photovoltaic uncertain variable; represents the electrical load uncertain variable; the matrixes A, C, D and F correspond to the variable coefficients in the constraint conditions, and the constant vectors are composed of B and E.

[0095] In addition, since the Wasserstein constraint in the model is a nonlinear term, the model is difficult to solve, and if the optimization problem is a convex problem, the distribution robustness problem can be equivalently converted into the following linear model:

[0096]

[0097] wherein, is the error of the uncertain variable, represents the error of the photovoltaic; represents the error of the electrical load; represents a function containing the error of the uncertainty; represents the error of the nth photovoltaic; represents the error of the nth electrical load; represents the sample number index; N is the sample number of uncertain variables.

[0098] In view of the fact that the reconstructed WDRO model contains a complex "min-max-min" nested structure, a column and constraint generation algorithm will be used for solving. The C&CG algorithm decomposes the optimization problem into a master problem and a sub-problem, and seeks the optimal result by means of alternating solution. The specific steps are as follows:

[0099] The objective function is set as the minimization of economic cost or carbon emission, the probability distribution of photovoltaic power output and electrical load power scene and the model parameters in the linearization model are initialized, and the lower bound, upper bound and iteration number are initialized;

[0100] According to the current iteration number, the master problem is solved to obtain the day-ahead scheduling strategy, and the lower bound is updated; then the sub-problem is solved to obtain the worst-case scenario probability distribution and the scheduling scheme, and the upper bound is updated;

[0101] If the termination iteration condition is met, the iteration is terminated, the worst-case probability distribution is returned, and the optimal scheduling scheme of the multi-energy complementary system is obtained; wherein the termination iteration condition is that the difference between the current upper bound and the current lower bound is less than a preset error; if the termination iteration condition is not met, the current iteration number is updated according to a preset rule, and the master problem and the sub-problem are solved according to the current iteration number respectively to obtain the worst-case probability distribution, and the current lower bound and the current upper bound are updated until the termination iteration condition is met, and the optimal scheduling scheme of the multi-energy complementary system is obtained.

[0102] In the iteration process, the master problem is constantly updated and saves the scene probability distribution information; when the model converges, the master problem constraint set has already contained all possible worst-case scenario probability distributions, so the solution of the master problem after convergence is the robust scheduling scheme of the ideal case.

[0103] After reconstruction by the C&CG algorithm, the model master problem can be described as:

[0104]

[0105] In the formula, as the first phase auxiliary parameter of CCG, represents the current iteration number, represents the upper limit of iteration number, represents the solution of the sub-problem obtained after the th iteration, , represents the worst-case value of the uncertain variable error under the worst-case scenario probability distribution after the th iteration.

[0106] The reconstructed sub-problem can be described as:

[0107]

[0108] wherein, represents the error of the uncertain variable; represents the known value of the first stage; represents the first Lagrange multiplier; represents the Lagrange multiplier defined by the equation; represents the second Lagrange multiplier.

[0109] It is difficult to solve the decomposed sub-problem because it is still a double-layer max-min decision. Therefore, the strong duality theorem is used to linearize the sub-problem, and the processed sub-problem is:

[0110]

[0111] wherein, represents the error of the nth uncertain variable.

[0112] The steps of solving the C&CG algorithm are as follows:

[0113] (1) Given the initial severe scenario probability distribution and the convergence threshold, set the iteration number l=1, and the initial upper and lower bounds .

[0114] (2) Substitute the scenario probability distribution into the main problem and solve to obtain the optimal solution .

[0115] (3) At this time becomes the new lower bound, and is brought into the linearized sub-problem to obtain the objective function and the worst probability distribution , and the upper bound is updated .

[0116] (4) If , the optimal solution is derived, and the iteration is ended; otherwise, the variable and the corresponding constraint are increased, l=l+1 is set, and b is returned.

[0117] In order to verify the effectiveness of the present application, simulation analysis is carried out based on an example. The example simulation analysis includes four aspects of sensitivity analysis of a multi-energy complementary system heating / cooling system, economic and low-carbon scheduling scheme, sensitivity analysis of a WDRO model, and comparison of optimization models.

[0118] Figure 2The influence of PMV evaluation index on the heating system in the building is shown in Fig. 1. It can be seen from the figure that the smaller the constraint range of PMV evaluation index, the higher the requirement of users on room temperature, and the smaller the fluctuation range of indoor temperature. At this time, the system needs to invest more heat load, which shows that the heat storage capacity of the heating system decreases. Therefore, appropriately increasing the constraint range can improve the flexibility of heat load and enhance the heat storage capacity of the system. The operator of the multi-energy complementary system can flexibly select appropriate parameters according to the actual operation status of the power plant, so as to effectively meet various scheduling requirements, improve the operation flexibility, and save energy.

[0119] When the main target of the system is to minimize the operation cost, the optimal scheduling scheme of the multi-energy complementary system is as shown in Fig. 2. Figures 3 to 6

[0120] In the period of 5:00-10:00, the photovoltaic output gradually increases, and the gas turbine runs at full power. At this time, the power generation in the system fully meets the demand of the electric load. Since the energy conversion efficiency of the electric-to-gas equipment is 60%, and the procurement cost of natural gas is 250 yuan / MWh, and the selling price of electricity is 200 yuan / MWh, 1 MWh of electricity sold directly to the large power grid can obtain a profit of 200 yuan, but the conversion into natural gas through the P2G device can only obtain a profit of 150 yuan, so the system sells the excess photovoltaic output to the large power grid to reduce the operation cost.

[0121] In the period of 12:00-16:00, the photovoltaic is in a high output state, and the electric power demand in the system is low. The distributed energy storage device is partially charged to absorb the excess photovoltaic power generation. At this time, the amount of electricity sold increases, and the system benefits from the excess photovoltaic electricity in the electricity market.

[0122] In the period of 17:00-20:00, the photovoltaic output gradually decreases, but the electricity demand in the system gradually reaches the peak. At this time, the gas turbine runs at full power. Since the purchase price of electricity is high, the energy storage device is discharged to reduce the peak load and reduce the purchase of high-priced electricity.

[0123] In the period of 21:00-4:00, the photovoltaic output is zero, and the electric load in the system is low. The gas turbine maintains partial output to meet the basic load. Since the purchase price of electricity is low at this time, and the selling price of electricity is unchanged, the system appears to purchase electricity, and the amount of electricity sold tends to zero. The energy storage device is charged to absorb low-priced electricity energy to support the electricity peak in the daytime.

[0124] In addition, the gas boiler and the waste heat boiler are responsible for providing heat load for the system. However, the total heat power output by the two at any time cannot meet the heat demand at the time, so the absorption refrigerator is not started.

[0125] ​When the main objective of the system is to achieve the minimization of carbon emissions, the optimal scheduling scheme of the multi-energy complementary system is as shown in Figures 7 to 10 When the minimum carbon emission is the optimization objective, the system scheduling prioritizes carbon reduction. Since the carbon emission per megawatt-hour in the process of generating electricity by purchasing natural gas to drive the gas turbine exceeds the carbon emission generated by directly purchasing electricity from the power grid, and the model does not include a power selling setting, there is no power buying and selling behavior in this scheduling scheme.

[0126] In the 15:00-16:00 period, the power load in the system is low, and the demand response load is shifted to run in this period. In addition, the power generation of the photovoltaic unit and the gas turbine meets the load demand, and there is no supply-demand gap, so the energy storage device is not called. Similarly, the gas boiler and the waste heat boiler bear the heat demand in the system, but at 24:00, the heat load power exceeds the heat supply capacity of the waste heat boiler, so the absorption refrigerator starts to run.

[0127] Table 1 compares the three methods used in the optimal scheduling of the multi-energy complementary system: robust optimization, two-stage distribution robust optimization, and random optimization. Among them, the robust optimization method is designed based on the two-stage robust optimization model, and the random optimization uses the sample average model. As shown in Table 1, when the two-stage distribution robust optimization method is used, the operating cost and carbon emission of the system increase compared to random optimization, but are still lower than robust optimization. This is because the WDRO model constructs an uncertainty set based on the Wasserstein distance, which contains all possible probability distribution information of renewable energy; random optimization only uses the sample empirical distribution to replace the true distribution; and two-stage robust optimization only considers the worst case in the fuzzy set, and the result is relatively conservative. Therefore, two-stage distribution robust optimization is more conservative than random optimization, but more optimistic than traditional robust optimization.

[0128] Table 1

[0129]

[0130] It can also be seen from Table 1 that, in the case of a small number of system samples, two-stage distribution robust optimization tends to adopt a conservative scheduling scheme; and as the number of samples increases, its scheduling strategy gradually tends to be more optimistic like random optimization. In addition, it can be seen from the table that, compared with the other two optimization models, the random optimization process takes the most time. This is mainly because random optimization needs to handle all the related constraints of the scenarios in the calculation process, and the solving time increases significantly with the increase of data. In comparison, although the increase of the number of samples also leads to the increase of the calculation time of two-stage distribution robust, this increase basically presents a linear trend, and still remains within an acceptable range in practical applications.

[0131] The above embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but cannot be understood as a limitation on the scope of the present application. For those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, which all belong to the protection scope of the present application.

Claims

1. A two-stage partial blue bar optimization scheduling method for a multi-energy complementary system, characterized in that, Includes the following steps: S1. Acquire a multi-energy complementary system that includes distributed photovoltaics, energy storage units, gas turbines, heating and cooling systems, and adjustable demand response resources; S2. Based on the uncertainty of photovoltaic output and electrical load in a multi-energy complementary system, an uncertainty set is constructed using Wasserstein distance, and then a two-stage sub-Bruker optimization model with a min-max-min structure is established. S3. Based on the two-stage bipolar bar optimization model, linearization technology is used to obtain a linearized model. Finally, the linearized model is solved by the column and constraint generation algorithm to obtain the optimal scheduling scheme of the multi-energy complementary system.

2. The method according to claim 1, characterized in that, The adjustable demand response resources include P2G devices; the heating and cooling systems include electric chillers, absorption chillers, gas boilers, and waste heat boilers.

3. The method according to claim 2, characterized in that, In S2, the set of uncertainties for: ； In the formula, This represents the 1-norm distance between the final probability distribution and the original probability distribution; This represents the 1-norm distance between uncertain variables; express , The joint probability distribution of ; The initial probability distribution values ​​for the sample of uncertain factors. , Represents the original photovoltaic probability distribution. This represents the probability distribution of the original electrical load; This represents the probability distribution value of adverse effects due to uncertain factors. , This represents the final photovoltaic probability distribution. This represents the probability distribution of the final electrical load. The radius of the 1-Wasserstein sphere; The radius of the ∞-Wasserstein sphere; The confidence level is 1-Wasserstein distance; It is the confidence level of the ∞-Wasserstein distance; , Obey respectively , random variables; Then it is The support set; This represents the probability that the 1-Wasserstein distance is less than a specified value. This represents the infinite norm distance between the final probability distribution and the original probability distribution; Indicates the infinite norm distance between uncertain variables; This represents the probability that the infinite-Wasserstein distance is less than a specified value.

4. The method according to claim 3, characterized in that, In S2, the objective function of the two-stage sub-Bruker optimization model is: ； ； ； ； ； ； in, , These are the day-ahead scheduling strategy and the optimal scheduling scheme, respectively. For the adverse probability distribution of uncertain factors, It is an uncertain set; Indicates the scheduling time; To reduce the charging and discharging costs of multi-energy complementary systems, For the power exchange cost of the upstream power grid of the multi-energy complementary system, For the demand response cost of a multi-energy complementary system, For the operating costs of gas turbines, The cost of purchasing gas for a multi-energy complementary system; This is the converted unit charge / discharge cost; This represents the discharge power of the energy storage unit at time t; This represents the charging power of the energy storage unit at time t; Indicates charge / discharge efficiency; Indicates the power purchased; Indicates the power output; These are the unit prices for the multi-energy complementary system to purchase and sell electricity to the upper-level power grid, respectively. Indicates the output power of the gas turbine; This indicates the unit cost of electricity generation from a gas turbine. Let t be the natural gas supply at time t; The unit energy cost of natural gas; Indicates the demand response coefficient; Indicates the upward power of the demand response; This indicates the downward power of the demand response.

5. The method according to claim 4, characterized in that, The constraint condition for the charging and discharging power of the energy storage unit at time t is: ； ； in, The capacity of the energy storage unit at the start of the operation; and This represents the maximum / minimum remaining capacity of the energy storage unit. The scheduling period; The constraint on the demand response cost of a multi-energy complementary system is: ； in, This represents the original demand response load power; This indicates the load power after demand response; The power generation capacity of the gas turbine and the constraints on its power generation capacity are as follows: ； ； in, This indicates the amount of natural gas consumed by the gas turbine; Indicates the efficiency of the gas turbine; and This indicates the upper and lower limits of the actual output of the gas turbine; State variables used to indicate the start and stop of the gas turbine; and This indicates the upper and lower limits of the gradeability of the gas turbine. Indicates the operating and shutdown time of the gas turbine; This indicates the minimum operating time and minimum downtime of the gas turbine.

6. The method according to claim 5, characterized in that, In S2, the constraints of the two-stage sub-Bruker optimization model include the output constraints of the P2G device, waste heat boiler, gas boiler, absorption chiller and electric chiller, as well as the energy balance constraints of the power grid, natural gas network, heating network and cooling building interior in the multi-energy complementary system.

7. The method according to claim 6, characterized in that, The output constraint of the P2G device is: ； ； in, This indicates the amount of natural gas consumed by P2G; This represents the electrical power generated by the P2G; The energy conversion efficiency of the P2G device; , These are the minimum and maximum output values ​​of the P2G device, respectively. The output constraints for waste heat boilers and gas-fired boilers are as follows: ； in, This indicates the thermal power generated by the waste heat boiler; This indicates the gas power consumed by the gas turbine; , These are the upper and lower limits of the output thermal power of the waste heat boiler, respectively. , These are the heat production efficiencies of waste heat boilers and gas-fired boilers, respectively. This indicates the thermal power generated by the gas-fired boiler; , These are the upper and lower limits of the output thermal power of the gas-fired boiler, respectively. The output constraints of absorption chillers and electric chillers are as follows: ； in, This indicates the cooling power absorbed from the refrigerator's output. This indicates the heat power consumed by the refrigeration unit; and These refer to the energy conversion efficiencies of absorption chillers and electric chillers, respectively. and These are the upper and lower limits of the output power of the absorption chiller, respectively. This indicates the electrical power consumed by the electric chiller; This indicates the cooling power generated by the electric chiller; , These represent the upper and lower limits of the output power of the electric chiller, respectively.

8. The method according to claim 7, characterized in that, The energy balance constraint of the power grid in a multi-energy complementary system is: ； in, This represents the actual photovoltaic output of the distributed photovoltaic system at time t; Indicates load power; This indicates the electrical power generated by electric refrigeration; Represents the binary coefficient for electricity purchase; This represents the maximum power exchanged between the multi-energy complementary system and the upstream power grid; S represents the conversion efficiency of distributed photovoltaic (PV); S represents the effective area of ​​the PV module in distributed PV; I represents the solar radiation intensity. This represents the ambient temperature at time t; This represents the minimum actual photovoltaic output of a distributed photovoltaic system at time t; This represents the maximum actual photovoltaic output of the distributed photovoltaic system at time t; The energy balance constraint of the natural gas network in a multi-energy complementary system is: ； Among them, among them, Indicates gas load; This indicates the gas power consumed by the gas turbine; This indicates the gas power consumed by the gas-fired boiler; The energy balance constraint of the heating network in a multi-energy complementary system is: ； in, Indicates heat load; This indicates the heat power consumed by the refrigeration unit; A coefficient representing the relationship between heat supply and supply / return water temperature; This indicates the water supply temperature of the heating pipes; This indicates the return water temperature of the heating pipes; This indicates the maximum water supply temperature; The indoor temperature of the building; The water supply temperature for the heating pipes; The thermal inertia of the heating system; Indicates the index coefficient; , , , , , Both are used to describe the thermal inertia of a heating system; For PMV (Product Value) indicators; This indicates the upper limit of fluctuation of the PMV index; M is the human body's energy metabolism rate, and W is the mechanical power generated by the human body. It is the ratio of the area of ​​the body covered by clothing to the area not covered. The surface heat transfer coefficient; The water vapor pressure of the environment in which the human body is located; Indicates the water temperature at the location; The temperature of the air in contact with the human body; These are the outer layer temperature of clothing and the average radiant temperature of the environment in which the human body is located, respectively. The energy balance constraints for the cooling room in a multi-energy complementary system are: ； ； ； In the formula, This represents the total cooling power at time t; These represent the indoor and outdoor temperatures, respectively; R and C represent the equivalent thermal resistance and equivalent heat capacity of the refrigerated building, respectively. It is the total cooling power at time t; Indicates the scheduling time interval; , These represent the upper and lower limits of the building's indoor temperature, respectively.

9. The method according to claim 8, characterized in that, In S3, the linearized model is: ； in, This represents the cost coefficient matrix for the first stage. This represents the cost coefficient matrix for the second stage. For the error of the uncertain variable, Indicates the error in photovoltaics; Indicates the error in electrical load; Represents a function that includes uncertainties and errors; This represents the error of the nth photovoltaic cell; This represents the error of the nth electrical load; The index represents the number of samples; N is the number of samples for the uncertain variable; A, C, D, and F are matrices, which are the coefficients of the variables in the corresponding constraints; B and E are both constant vectors.

10. The method according to claim 9, characterized in that, In S3, the optimal scheduling scheme for the multi-energy complementary system is obtained by using a column and constraint generation algorithm on the linearized model; including: The objective function is set as minimizing economic cost or carbon emissions. The probability distributions of photovoltaic power generation output and electrical load power scenarios are initialized, as well as the model parameters in the linearized model. The lower bound, upper bound, and number of iterations are also initialized. Based on the current iteration count, solve the main problem to obtain the day-ahead scheduling strategy and update the lower bound; then solve the subproblems to obtain the worst-case scenario probability distribution and scheduling scheme, and update the upper bound. If the termination iteration condition is met, the iteration terminates, the worst-case probability distribution is returned, and the optimal scheduling scheme of the multi-energy complementary system is obtained. The termination iteration condition is that the difference between the current upper bound and the current lower bound is less than a preset error. If the termination iteration condition is not met, the current iteration number is updated according to a preset rule, and the main problem and sub-problems are solved again based on the current iteration number to obtain the worst-case probability distribution. The current lower bound and the current upper bound are then updated until the termination iteration condition is met, and the optimal scheduling scheme of the multi-energy complementary system is obtained.

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