Park energy system energy optimization scheduling method based on efficiency evaluation

Abstract

The invention provides a park energy system energy optimization scheduling method based on efficiency evaluation. The method can solve the problems of low unit efficiency and high equipment vacancy rate in an existing scheduling scheme. According to the method, an energy optimization scheduling framework with game cross efficiency evaluation and a commercial solver as the core is provided, game cross efficiency is used for evaluating the efficiency value of operation of each unit, an efficiency item is constructed, the efficiency item is incorporated into a target function, and comprehensive optimization of economic cost, environmental cost and operation efficiency is achieved; the method comprises four parts of source-load-storage modeling, efficiency evaluation model modeling, target, constraint and parameter setting, and cyclic solution and improvement. According to the method, the operation efficiency value of each unit of the park energy system can be evaluated, so that the overall operation efficiency of the system is optimized, the operation cost is reduced, and the environmental pollution is reduced.

Description

Technical Field:

[0001] The present invention belongs to the fields of power dispatching and operations research, and relates to an energy optimization dispatching method for a park energy system based on efficiency evaluation, which is applied to the energy dispatching link in the park energy system. By evaluating the efficiency values of each unit in the energy system, the dispatching strategy is optimized to promote the improvement of the system operation efficiency. Background Art:

[0002] The energy systems used in traditional parks have problems such as a single energy structure, low efficiency, high cost, and lack of intelligent management. For this reason, modern parks are gradually turning to the construction of a park energy Internet to improve the efficiency, safety, and sustainability of energy utilization. However, the park energy system is often a weak link in the distribution network. Coupled with the uninterruptibility of park operations and the randomness of new energy power generation in the park, the park faces double challenges of production safety and energy security. In response to this problem, researchers have proposed that multi-energy complementarity is an important method to improve the safety and stability of the energy system.

[0003] As a carbon-neutral fuel, biomass has attracted more and more attention from researchers. At present, biomass power generation technologies include direct combustion power generation of biomass, gasification power generation, pyrolysis power generation, co-combustion, etc. However, the above methods all focus on using biomass as fuel for power generation, and have not studied the heat energy in the flue gas generated by combustion.

[0004] Secondly, there are many units in the energy system and the energy conversion relationship is complex. How to reasonably allocate and dispatch these diverse resources in the park energy system is still an urgent problem to be solved. The park energy system dispatching methods include intelligent grid optimization dispatching strategies, uncertain optimization dispatching methods, dispatching optimization methods based on deep reinforcement learning, etc. However, the above methods only use the operating cost and environmental cost as the objective function, and have not paid attention to the efficiency of the units and the overall operation efficiency of the energy system.

[0005] In summary, the existing research has the following problems: 1) The utilization method of biomass is single, resulting in waste of heat in high-temperature flue gas; 2) The efficiency and changes of each unit in the operation of the energy system have not been paid attention to.

[0006] Therefore, the present invention proposes an energy optimization dispatching method for a park energy system based on efficiency evaluation. This scheme evaluates the efficiency of each unit through game cross-efficiency, innovatively incorporates the efficiency value into the evaluation index of the dispatching scheme, optimizes the resource allocation from the efficiency perspective, and reduces the equipment idle rate. At the same time, the biomass cogeneration technology is introduced to achieve dual utilization of electricity and heat. This innovative method helps to provide new optimization ideas for energy dispatching and promotes the transformation of the energy dispatching system from a two-dimensional perspective of economic cost and environmental pollution to a three-dimensional perspective of economic cost, environmental pollution, and operation efficiency. Summary of the Invention:

[0007] The present invention proposes an energy optimization scheduling method for a park energy system based on efficiency evaluation, which can solve the problems of low unit efficiency and high equipment idle rate in the existing scheduling scheme. The present invention proposes an energy optimization scheduling scheme with game cross-efficiency evaluation and a commercial solver as the core, and takes a park energy system including a biomass co-firing waste heat and power generation unit, an electrolyzer, and a fuel cell as an example for experimental verification. First, establish a steady-state operation model of a biomass co-firing waste heat and power generation unit, a hydrogen fuel cell, an electrolyzer, a hydrogen storage tank, a heat storage tank, and a battery; then, considering the operating cost and environmental fines as the objective function, establish a low-carbon economic scheduling model; introduce the game cross-efficiency evaluation method to evaluate the efficiency values of the outputs of each unit and further evaluate the overall efficiency value of the park energy system; construct an efficiency term based on the efficiency values of each unit, and use the efficiency term, operating cost, and environmental fines as the objective function together to establish an optimal scheduling model. Finally, based on the real source-load data, a case simulation is carried out to verify the rationality and superiority of the proposed method. The present invention includes four steps: the first step: source-load-storage modeling; the second step: efficiency evaluation model modeling; the third step: setting goals, constraints, and parameters; the fourth step: iterative solution and improvement.

[0008] Source-load-storage modeling. In the energy system, "source-load-storage" refers to three key components: power source, load, and energy storage. The energy side of the present invention includes a large power grid, a biomass co-firing waste heat and power generation unit, a wind power generation unit, and a photovoltaic power generation unit; the load side includes: electric load, heat load, cold load, electric heating unit, and electrolyzer; the energy storage unit includes: battery, hydrogen storage tank, and heat storage tank. The biomass co-firing waste heat and power generation unit specifically includes an incineration power generation unit (IG) and an anaerobic biogas production unit (AB). As shown in the attached Figure 1 specification, the garbage classification mathematical model is:

[0009]

[0010] where m L (t) is the total amount of garbage received by the energy conversion unit in the t-th period, m Dry (t), m Wet (t) are the amounts of dry garbage and wet garbage in the t-th period; λ Dry and λ Wet are the proportions of dry garbage and wet garbage in the total amount. The dry garbage and biomass fuel are mixed and incinerated to obtain high-temperature flue gas. The anaerobic biogas production unit sends the wet garbage and the park's organic sewage into the fermentation tank, and uses anaerobic fermentation technology to convert organic matter into biogas. The incineration power generation unit model is:

[0011]

[0012] where V IG(t) is the volume of high-temperature flue gas generated by the incineration of biomass mixed waste, P IG (t) represents the electricity generated by the incineration unit, η comb is the operating efficiency of the combustion chamber, m Bio is the mass of biomass fuel incorporated at time t, η G,e is the steam boiler efficiency, μ G is the power supply efficiency of the steam turbine. λ D is the mass ratio of dry waste, λ B is the proportion of biomass; α Dry is the calorific value of dry waste combustion, α Bio is the calorific value of biomass combustion. The anaerobic biogas production unit model is:

[0013]

[0014] Among them, V se (t), P se (t) represents the volume of sewage after treatment and the electricity consumption for sewage treatment during the t period; η se represents the volume of sewage that can be treated by unit electric energy, α se represents the coefficient of fermentable organic matter in sewage; m se (t) represents the volume of fermentable sewage generated during the t period, ρ se represents the density of sewage after standing; P AB (t), P Bio (t) represents the amount of biogas generated and the output of biogas after purification during the t period, η B , η Bio are the biogas production coefficient and biogas purification coefficient of the biogas digester. The electric output power of the cogeneration unit includes the net output power and the fixed power consumption for biogas treatment:

[0015]

[0016] Among them, the heat output amount of the energy conversion unit is uniformly converted into the electricity unit. P chp-p (t), P chp-h (t) represents the net electric output power and the net heat output power of the energy conversion unit during the t period, λ p , λ h respectively represent the electricity production coefficient and heat production coefficient of the energy conversion unit.

[0017] In the hydrogen production-storage-utilization model, the most important component is the electrolyzer, which is used to electrolyze water to produce hydrogen. The operating models of the electrolyzer and fuel cell are:

[0018]

[0019] Among them, is the power of the electrolyzer to produce hydrogen by electrolyzing water at time t; is the hydrogen production efficiency of the electrolyzer, is the input electric power of the electrolyzer during the t period; P GE,H is the rated power of the electrolyzer; a, b, c represent the hydrogen storage tank content corresponding to the t period, respectively represent the power of the fuel cell to output electric energy, the hydrogen-electric conversion efficiency, and the hydrogen input power; is the hydrogen content of the hydrogen storage tank at time t, η ch,H is the hydrogen filling efficiency of the hydrogen tank, is the hydrogen filling power at time t, and respectively represent the upper and lower limits of the hydrogen injection power, and respectively represent the hydrogen release power of the hydrogen storage tank.

[0020] For efficiency evaluation model modeling, in the present invention, each unit in the energy system is used as a decision-making unit in hours, and the game cross-efficiency model is used to comprehensively evaluate the efficiency values of each unit.

[0021] The cross-efficiency model is the basis of game cross-evaluation. Suppose there are n decision-making units participating in the evaluation, and each decision-making unit DMU j uses m different inputs to obtain s different outputs. The i-th input and the r-th output of DMU j (j = 1, 2,..., n) are respectively denoted as x ij (i = 1,..., m) and y rj (r = 1,..., s). For any given decision-making unit, denoted as DMU d , its efficiency value can be calculated by formula (6):

[0022]

[0023] Obtain the optimal weight corresponding to each evaluated unit DMU d (d = 1,..., n), The d-cross efficiency of DMU j (j = 1,..., n) is:

[0024]

[0025] For DMU j (j = 1,...n), the average value of all E dj (d = 1,...n), that is:

[0026]

[0027] Ej can be used to represent DMU j (j = 1,...n) cross-efficiency values. The game cross-efficiency model is an improvement on the original basis. Assume that the decision-making unit DMU d has an efficiency value of α d , and other decision-making units DMU j (j ≠ d) will maximize their corresponding efficiency values on the condition that the efficiency value α d of DMU d is not reduced. Assume that the game d-cross-efficiency value of DMU j (relative to DMU d ) is:

[0028]

[0029] where, and are the feasible weights derived from formula (6), and α dj represents that the decision-making unit DMU j (j ≠ d) is only allowed to optimize its respective weights on the condition of not damaging the efficiency value α d of DMU d . The weights in formula (9) are not restricted to be optimal, only requiring to be a feasible solution of formula (6). Based on the above assumptions, a non-cooperative game model is used to determine the final game d-cross-efficiency. For each decision-making unit DMU j , there is:

[0030]

[0031] where, the initial value of α d is the cross-efficiency value of DMU d (according to formula (8)), that is, it is restricted that α d ≤1. As the game solution process progresses, α d gradually converges to the optimal game cross-efficiency value. Model (10) represents the game cross-efficiency of DMU j with respect to DMU d . The game cross-efficiency value of DMU d will not be lower than its cross-efficiency value. For DMU j (j ≠ d), formula (10) will be calculated n times for each d = 1,...,n in total.

[0032] The selection of input indicators and output indicators determines the physical meaning in the physical world corresponding to the game cross-efficiency value. In the game cross-efficiency evaluation, each unit is used as a decision-making unit under each hourly dimension. The maintenance cost, depreciation cost, carbon emission, unit capacity, and energy loss of each device are used as the input indicators of the decision-making unit, and the power output is used as the output indicator of the decision-making unit. Standardization measures are adopted to linearly map all data to the interval between 0.1 and 1, eliminating the influence caused by the too large difference in dimension.

[0033] Determine the number n of decision-making units DMU, the m input indicators of each decision-making unit, and the s output indicators of each decision-making unit, and the three satisfy the constraints:

[0034] n≥max{m·s,3·(m + s)} (11)

[0035] Since there cannot be a large number of 0s or negative numbers in the original data, otherwise it will cause data errors. Therefore, the input indicators are normalized:

[0036]

[0037] In the formula, X is the data to be normalized, X max and X min respectively represent the maximum and minimum values in X, a and b respectively represent the lower and upper limits of normalization, and X o represents the data after normalization.

[0038] The present invention takes the minimum system operation cost and the minimum carbon emission cost as the original objective function, and adds an efficiency term to form a new objective function for optimal scheduling. The constraint conditions mainly include power constraint and output constraint.

[0039] The objective function of the system is:

[0040] minF=F1+F2+F3 (13)

[0041] In the formula, F represents the total objective function, and F1, F2, and F3 respectively represent the operation cost, carbon emission cost, and efficiency term. The operation cost F1 is expressed as:

[0042]

[0043] In the formula, respectively represent the operation costs of photovoltaic, wind power, power purchase and sale, electrolyzer, hydrogen fuel cell, combined heat and power unit power generation, heat production, battery, hydrogen storage tank, and heat storage tank at time t. T = 24 represents that the scheduling time scale is 24 hours. The carbon emission cost F2 is expressed as:

[0044]

[0045] In the formula, represents the implicit carbon emissions generated from purchasing electricity from the large power grid, and respectively represent the carbon emissions from power generation and heat generation of the combined heat and power unit.

[0046]

[0047] In the formula, α1 to α9 are the weight coefficients of each item. In this paper, it is assumed that the power generation of wind power and photovoltaic power is fully absorbed. Therefore, it is not restricted by the efficiency term, so α1 = 0 and α2 = 0. respectively represent the average values of the game efficiency values of photovoltaic, wind turbine, hydrogen fuel cell, combined heat and power - power supply, combined heat and power - heat supply, electrolyzer, hydrogen storage tank, heat storage tank, and battery within 24 hours, which are obtained by the following formula:

[0048]

[0049] respectively represent the output powers of photovoltaic, wind turbine, hydrogen fuel cell, combined heat and power - power supply, combined heat and power - heat supply, electrolyzer, hydrogen storage tank, heat storage tank, and battery at time t. The operation cost expression is as follows:

[0050] 1) The operation costs of photovoltaic and wind turbine power generation are respectively:

[0051]

[0052] In the formula, and represent the operation and maintenance costs of photovoltaic and wind turbine, and represent the depreciation cost per kWh per unit time.

[0053] 2) The operation cost of the large power grid is as follows:

[0054]

[0055] In the formula, and respectively represent the electricity purchase price and electricity sale price of the large power grid at time t, represents the operation and maintenance cost of the line and transformer when purchasing electricity from the large power grid, and respectively represent the electricity purchase power and electricity sale power at time t. It is set in the code that electricity purchase and electricity sale from the large power grid cannot be carried out simultaneously.

[0056] 3) The operation costs of the electrolyzer and hydrogen fuel cell are as follows:

[0057]

[0058] In the formula, represents the power consumption of the electrolyzer, and respectively represent the operation and maintenance cost and depreciation cost of the electrolyzer, and respectively represent the operation and maintenance cost and depreciation cost of the hydrogen fuel cell.

[0059] 4) The operating cost of the combined heat and power unit is as follows:

[0060]

[0061] and respectively represent the electricity generation and heat production of the combined heat and power unit at time t, and respectively represent the operation and maintenance cost, depreciation cost, and fuel cost of the electricity generation and heat production of the unit.

[0062] 5) The operating cost of the energy storage device:

[0063]

[0064] In the formula, and respectively are the charging power, operation and maintenance cost, and depreciation cost of the battery, hydrogen storage tank, and heat storage tank.

[0065] The carbon emission cost expression is as follows:

[0066] 1) The implicit carbon emission of purchasing electricity from the large power grid:

[0067]

[0068] In the formula, δ Grid is the carbon emission coefficient of purchasing electricity from the large power grid.

[0069] 2) The carbon emissions of electricity generation and heat production of the combined heat and power unit:

[0070]

[0071] In the formula, δ CHP-p and δ CHP-h respectively correspond to the carbon emission coefficients of heat production and electricity generation of the biomass co-fired waste combined heat and power unit.

[0072] Set the power constraint conditions.

[0073] 1) Electric power balance constraint

[0074]

[0075] Wherein, and are the output electric powers of the photovoltaic power generation unit and the wind power generation unit in the t period, and represent the electricity purchase and sale volumes of the microgrid from / to the large power grid, is the output electric power of the combined heat and power unit, is the output power of the hydrogen fuel cell, and are the discharge and charge powers of the storage battery, respectively. Electric load, Power consumption of the electrolyzer, is the power consumption of the electric heating equipment.

[0076] 2) Thermal power balance constraint

[0077]

[0078] Wherein, represents the heat production of the combined heat and power unit, represents the heat production of the electric heating equipment, and represent the injection and release heat powers of the heat storage tank, respectively, represents the thermal load value, represents the heat consumption of the absorption chiller.

[0079] 3) Cooling power balance constraint

[0080]

[0081] Wherein, represents the cooling power of the absorption chiller at time t, represents the cooling load at time t.

[0082] Each unit in the energy system is limited by the actual unit capacity, etc., so the output constraints are set as follows:

[0083] 1) Constraint of the biomass co-fired waste combined heat and power unit (co-firing ratio < 20%)

[0084]

[0085] Wherein, and are the upper and lower limits of the dry waste that the energy conversion unit can process in the t period, and are the upper and lower limits of the biomass fuel burned by the energy conversion unit, and are the upper and lower limits of the output electric power of the energy conversion unit, and are the upper and lower limits of the thermal power output of the energy conversion unit, and are the upper and lower limits of the power ramp rate of the electrical power output of the energy conversion unit, and are the upper and lower limits of the heat output ramp of the energy conversion unit, restrict the thermal power of the energy conversion unit to be less than the maximum power that can be produced at the current moment.

[0086] 2) Constraints on the electrolytic hydrogen and hydrogen fuel cell models

[0087]

[0088] In the formula, and respectively represent the upper and lower limits of the input electrical power of the electrolyzer hydrogen production equipment, and represent the ramp constraint for hydrogen production by the electrolyzer, and represent the ramp constraint for power-to-hydrogen, and respectively represent the upper and lower limits of the electrical power output of the hydrogen fuel cell, and represent the power output ramp constraint of the fuel cell, and respectively represent the upper and lower limits of the hydrogen-to-electricity conversion efficiency values of the hydrogen fuel cell.

[0089] 3) Constraints on the battery output, heat storage tank output, and hydrogen storage tank

[0090]

[0091] and respectively represent the upper and lower limits of the charge and discharge power of the battery, represents the state of charge of the battery at time t, and represent the upper and lower limits of the state of charge of the battery.

[0092] 4) Constraints on the heat storage tank output

[0093]

[0094] and represent the upper and lower limits of the heat release power of the heat storage tank, and represent the upper and lower limits of the heat input power of the heat storage tank, and represent the upper and lower limits of the capacity of the heat storage tank.

[0095] 5) Constraints on the hydrogen storage tank output

[0096]

[0097] and represent the upper and lower limits of the hydrogen storage tank. and represent the upper and lower limits of the charging and discharging power of the hydrogen storage tank respectively. represent the hydrogen storage tank capacity, hydrogen output power, and hydrogen injection power at time t respectively.

[0098] 6) Thermal equipment output constraint

[0099]

[0100] In the formula, and represent the heat production and power consumption of the electric heating equipment at time t respectively, and η eh i.e., the electro-thermal conversion ratio of the electric heating equipment, takes a fixed value here. and are the upper and lower limits of the power consumption of the electric heating equipment, and are the upper and lower limits of the heat production of the electrolysis equipment.

[0101] 7) Absorption chiller constraint

[0102]

[0103] In the formula, represents the cooling capacity of the absorption chiller at time t, represents the heat absorption of the chiller at time t, and η AC represents the heat-cold conversion rate of the absorption chiller. The power consumption of this unit is summarized into the unified load curve, and no separate power conversion formula is set. and represent the upper and lower limits of the cooling capacity of the absorption chiller.

[0104] 8) Large power grid power purchase and sale constraint

[0105]

[0106] In the formula, and are the upper and lower limits of the power purchased by the microgrid from the large power grid and the power sold to the large power grid at time t respectively.

[0107] The electric load, heat load, wind power generation output, and photovoltaic power generation output of typical days in each season involved in this paper all come from measured data, with the time ranging from July 2023 to July 2024. The purchase and sale of electricity from the large power grid adopt the local time-of-use electricity price, and the natural gas price is a fixed price of 0.35 yuan / (kW·h). The established model calls the YALMIP toolbox on the MATLAB platform and uses the commercial solver Gurobi for solution. The scheduling period is 24h, and the scheduling accuracy is 1h. The number of iterations of the game cross-efficiency evaluation model is set to 30 times. The main parameters of each of the other units include installed capacity, upper and lower limits of output, generation cost, operation and maintenance cost, energy conversion efficiency, etc.

[0108] Solve and improve. Use the original objective function to solve the original scheduling plan through the Gurobi solver to obtain the output of each unit in the next 24 hours. Based on the above results, use the game cross-efficiency to evaluate the relative efficiency values of each unit in the next 24 hours. Then, incorporate these efficiency values into the new objective function, use the Gurobi solver to solve the new scheduling plan and save it. Set the number of loops until the loop ends, and output the saved best scheduling plan as the final scheduling strategy.

[0109] The present invention has the following advantages and effects compared with the prior art:

[0110] (1) Accurately evaluate the relative efficiency of each unit: Through the game cross-efficiency evaluation method, the relative efficiency values of each unit can be accurately evaluated. At the same time, the mutual influence between units is considered, comprehensively reflecting the operating efficiency of the system, and providing data support for subsequent optimal scheduling.

[0111] (2) Efficiently schedule resources: The rationality of unit capacity configuration can be reflected through the efficiency value, minimizing the idle time of equipment as much as possible and enhancing the participation of units in energy scheduling.

[0112] (3) Multi-dimensional energy scheduling: The addition of the efficiency term can better integrate and utilize renewable energy sources such as solar energy and biomass, further reducing dependence on fossil fuels and lowering carbon emissions. At the same time, the introduction of the efficiency term enables the scheduling system to dynamically adjust the operating status of each unit according to real-time data, achieving more refined control and management. Description of the drawings:

[0113] Figure 1 It is the schematic diagram of the biomass co-firing waste heat and power cogeneration unit of the method of the present invention.

[0114] Figure 2 It is the implementation flow block diagram of the method of the present invention.

[0115] Figure 3 It is the program flow chart of the method of the present invention. Specific implementation mode:

[0116] A method for optimizing the energy scheduling of a park energy system based on efficiency evaluation proposed by the present invention is described in detail with reference to the accompanying drawings as follows:

[0117] Figure 1 It is a schematic diagram of a biomass co-fired waste thermoelectric co-generation unit of the method of the present invention. This figure shows the working principle of the biomass co-fired waste thermoelectric co-generation unit.

[0118] Figure 2 It is a flowchart of the implementation process of the method of the present invention. The method is divided into four steps, namely: the first step is source-load-storage modeling; the second step is efficiency evaluation model modeling; the third step is to set goals, constraints and parameters; the fourth step is iterative solution and improvement. First, model the biomass co-fired waste thermoelectric co-generation unit and the hydrogen production-storage-utilization model. Secondly, model the efficiency evaluation model, including scheduling model modeling and selection of input and output indicators. Then, set goals, constraints and parameters. Finally, perform iterative solution and improvement.

[0119] Figure 3 It is a program flowchart of the method of the present invention. j represents the total number of loops of the current program, I represents the number of convergence solutions of the game cross efficiency, and K represents the serial number of the decision-making unit. The number of units and the number of decision-making units are fixed values. The program starts to execute. First, model each unit, set parameters, and import new energy and electro-thermal load data. Start the first loop, obtain the output data of each unit based on the original objective function, and save the scheduling result. Then, judge whether the loop count is exceeded. If the loop count is not exceeded, calculate the game cross efficiency value, obtain the efficiency values of all units by judging whether the efficiency value converges, and improve the original objective function according to the weight coefficient and loop the objective function again until the loop count is exceeded, and then output the optimal scheduling strategy; if it has exceeded, directly output the optimal scheduling strategy.

Claims

1. An energy optimization scheduling method for a park energy system based on efficiency evaluation. The method consists of Step 1: Modeling of source, load and storage; Step 2: Modeling of efficiency evaluation model; Step 3: Setting goals, constraints and parameters; and Step 4: Iterative solution and improvement. The present invention applies the game cross-efficiency evaluation method to incorporate the operation efficiency value of the unit into the evaluation index of the scheduling method, optimizes the scheduling of the park energy system from the perspective of the efficiency value, realizes the efficient and reasonable operation among various units, and provides a reasonable decision-making basis for the long-term stable operation of the system. It is characterized in that: The steps of the method are as follows: (1) Source-load-storage modeling In the park energy system, "source-load-storage" refers to the three key components of power supply, load, and energy storage; the energy side includes the large power grid, biomass co-fired waste thermoelectric cogeneration unit, wind turbine generator, and photovoltaic generator; The load side includes: electrical load, thermal load, cooling load, electric heating unit, electrolyzer; the energy storage unit includes: battery, hydrogen storage tank, heat storage tank; the main component modeling is as follows: 1) Biomass co-fired waste thermoelectric cogeneration unit The biomass co-firing power generation technology adopts direct co-combustion, and the energy conversion and treatment unit includes two parts: the incineration power generation unit (IG) and the anaerobic biogas production unit (AB); the waste classification mathematical model is: Among them, m L (t) is the total amount of garbage received by the energy conversion treatment unit in the t time period, m Dry (t), m Wet (t) are the amounts of dry garbage and wet garbage in the t time period; λ Dry and λ Wet are the proportions of dry garbage and wet garbage in the total amount; the dry garbage and biomass fuel are mixed and incinerated to obtain high-temperature flue gas, and the anaerobic biogas production unit sends the wet garbage and the organic sewage in the park into the fermentation tank, and uses anaerobic fermentation technology to convert organic matter into biogas; the incineration power generation unit model is: Among them, V IG (t) is the volume of high-temperature flue gas generated by the incineration of biomass mixed waste, P IG (t) represents the electricity generated by the incineration unit, η comb is the operating efficiency of the combustion chamber, m Bio is the mass of biomass fuel incorporated at time t, η G,e is the steam boiler efficiency, μ G is the power supply efficiency of the steam turbine; λ D is the mass ratio of dry waste, λ B is the proportion of biomass; α Dry is the calorific value of dry waste combustion, α Bio is the calorific value of biomass fuel combustion; The anaerobic biogas production unit model is: Among them, V se (t), P se (t) represent the volume of sewage after treatment and the power consumption of sewage treatment within the time period t; η se represents the volume of sewage that can be treated per unit of electric energy, and α se represents the coefficient of fermentable organic matter in sewage; m se (t) represents the volume of fermentable sewage generated within the time period t, and ρ se represents the density of sewage after standing; P AB (t), P Bio (t) represent the amount of biogas generated and the biogas output after purification within the time period t, η B , η Bio are the biogas production coefficient and biogas purification coefficient of the biogas digester; The electric output power of the cogeneration unit includes the net output power and the fixed power consumption for biogas treatment: Among them, the heat output amount of the energy conversion unit is uniformly converted into the unit of electric energy; P chp-p (t), P chp-h (t) represents the net electric power output and the net heat power output of the energy conversion unit in the t time period, λ p , λ h respectively represent the power generation coefficient and the heat generation coefficient of the energy conversion unit; 2) Hydrogen production-storage-utilization model modeling The hydrogen production-storage-utilization model adopts a proton exchange membrane electrolyzer, sets the efficiency of the electrolyzer, hydrogen storage tank, and hydrogen fuel cell to the rated value, and only considers its power supply capacity; the operation models of the electrolyzer hydrogen production unit and the fuel cell can be expressed as: Among them, P t GE,H is the power of the electrolyzer to produce hydrogen by electrolyzing water at time t; is the hydrogen production efficiency of the electrolyzer, and P t GE,e is the input electric power of the electrolyzer during the t period; P GE,H is the rated power of the electrolyzer; a, b, c represent the hydrogen storage tank content corresponding to the t period, and P t HFC,e , η HFC , respectively represent the power of the fuel cell to output electric energy, the hydrogen-electric conversion efficiency, and the hydrogen input power; is the hydrogen content in the hydrogen storage tank at time t, and η ch,H is the hydrogen filling efficiency of the hydrogen tank, and P t ch,H is the hydrogen filling power at time t, and respectively represent the upper and lower limits of the hydrogen injection power, and respectively represent the hydrogen release power of the hydrogen storage tank; (2) Efficiency evaluation model modeling The present invention takes each output unit in the energy system as a decision-making unit and uses the game cross-efficiency model to evaluate the efficiency value of each unit; 1) Cross-efficiency model The cross-efficiency model is the basis for game cross-evaluation. Suppose there are n decision-making units participating in the evaluation, and each decision-making unit DMU j uses m different inputs to obtain s different outputs; DMU j (j = 1, 2,..., n) the i-th input and the r-th output are respectively denoted as x ij (i = 1,..., m) and y rj (r = 1,..., s); for any given decision-making unit, denoted as DMU d , its efficiency value can be calculated by formula (6): Obtain each evaluated decision-making unit (DMU) d (d = 1, ..., n) corresponding optimal weights DMU j (j = 1, ..., n) the d-cross efficiency is: For the DMU j (j = 1,...n), all E dj (d = 1,...n) of the average value, that is: can be used to represent the DMU j (j = 1,...n) cross-efficiency value; 2) Game cross-efficiency model The game cross-efficiency model is improved on the original basis, assuming that the efficiency value of the decision-making unit DMU d is α d , and other decision-making units DMU j (j≠d) will maximize their corresponding efficiency values on the condition that the efficiency value α d of DMU d is not reduced; assuming that the game d-cross-efficiency value of DMU j (relative to DMU d ) is as follows: Among them, and are the feasible weights derived from formula (6), and α dj represents the decision-making unit DMU j (j≠d) is only allowed to optimize its respective weights without damaging the efficiency value α d of DMU d ; the weights in formula (9) are not restricted to be optimal, but only required to be a feasible solution of formula (6); based on the above assumptions, a non-cooperative game model is adopted to determine the final game d-cross efficiency; for each decision-making unit DMU j , there is: Among them, α d is initially set to the cross-efficiency value of DMU d (according to formula (8)), that is, α d is limited to ≤ 1. As the game-solving process progresses, α d gradually converges to the optimal game cross-efficiency value; Model (10) represents the game cross-efficiency of DMU j with respect to DMU d ; The game cross-efficiency value of DMU d will not be lower than its cross-efficiency value. For DMU j (j ≠ d), formula (10) will be calculated n times for each d = 1,..., n in total; 3) Selection of input indicators and output indicators The selection of input indicators and output indicators determines the physical meaning of the game cross-efficiency value. The game cross-efficiency evaluation takes each unit as a decision-making unit under each hour dimension, and uses the maintenance cost, depreciation cost, carbon emissions, unit capacity, and energy loss of each device as the input indicators of the decision-making unit, and the power output as the output indicator of the decision-making unit. And adopt standardization measures to linearly map all data to the interval between 0.1 and 1 to eliminate the influence caused by too large a difference in dimensions; Determine the number n of decision-making units DMU, the m input indicators of each decision-making unit, and the s output indicators of each decision-making unit, and the three satisfy the constraint: n≥max{m·s,3·(m+s)} (11) Since there cannot be a large number of 0s or negative numbers in DEA, the input indicators are normalized: Where X is the data to be normalized, X max and X min represent the maximum and minimum values in X respectively, and a and b represent the lower and upper limits of normalization respectively; (3) Set goals, constraints, and parameters The present invention takes the minimum system operation cost and the minimum carbon emission cost as the objective function, and uses the unit efficiency term as the supplementary objective function for optimal scheduling; the constraint conditions mainly include power constraints and output constraints; The objective function of the system is: minF=F1+F2+F3 (13) In the formula, F represents the total objective function, and F1, F2, and F3 respectively represent the operation cost, carbon emission cost, and efficiency term; the operation cost F1 is expressed as: In the formula, respectively represent the operating costs of photovoltaic, wind power, power purchase and sale, electrolyzer, hydrogen fuel cell, combined heat and power unit for power generation, heat production, battery, hydrogen storage tank, and heat storage tank at time t; T = 24 indicates that the scheduling time scale is 24 hours; In the formula, represents the implicit carbon emissions generated from purchasing electricity from the large power grid, and respectively represent the carbon emissions from power generation and heat generation of the cogeneration unit; Wherein, α1 to α9 are the weight coefficients of each term. In this paper, it is assumed that the power generation of wind power and photovoltaic power is fully absorbed. Therefore, it is not restricted by the efficiency term, so α1 = 0 and α2 = 0. respectively represent the mean values of the game efficiency values of photovoltaic, wind turbine, hydrogen fuel cell, combined heat and power - power supply, combined heat and power - heat supply, electrolyzer, hydrogen storage tank, heat storage tank, and battery within 24 hours, and are obtained by the following formula: P t PV ,P t PW , P t CHP-p ,P t CHP-h ,P t Pel , P t HS ,P t Bat respectively represent the output powers of photovoltaic, wind turbine, hydrogen fuel cell, combined heat and power - power supply, combined heat and power - heat supply, electrolyzer, hydrogen storage tank, heat storage tank, and battery at time t; The operation cost expression of each unit is as follows: 1) The operation costs of photovoltaic and wind power generation are respectively: In the formula, and represent the operation and maintenance costs of photovoltaic and wind turbines, and represent the depreciation cost per kWh per unit time; 2) The operation cost of the large power grid is as follows: Wherein, and respectively represent the electricity purchase price and the electricity selling price of the large power grid at time t, represents the operation and maintenance cost of the line and transformer when purchasing electricity from the large power grid, P t buy and P t sell respectively represent the electricity purchase power and the electricity selling power at time t, and it is set in the code that electricity purchase and electricity selling from the large power grid cannot be carried out simultaneously; 3) The operation costs of the electrolyzer and the hydrogen fuel cell are as follows: Wherein, P t Pel represents the power consumption of the electrolyzer, and represent the operation and maintenance cost and depreciation cost of the electrolyzer respectively, and represent the operation and maintenance cost and depreciation cost of the hydrogen fuel cell respectively; 4) The operation cost of the thermoelectric cogeneration unit is as follows: P t CHP-p and P t CHP-h respectively represent the electricity generation and heat generation of the combined heat and power unit at time t, and respectively represent the operation and maintenance cost, depreciation cost, and fuel cost of the electricity generation and heat generation of the unit; 5) The operation cost of the energy storage device: where P t bat_cha , P t HS_cha , and are the charging power, operation and maintenance cost, and depreciation cost of the battery, hydrogen storage tank, and heat storage tank, respectively; The carbon emission cost expression is as follows: 6) The implicit carbon emissions from purchasing electricity from the large power grid: where δ Grid is the carbon emission factor for purchasing electricity from the large power grid; 7) The carbon emissions from power generation and heat production of the thermoelectric cogeneration unit: where δ CHP-p and δ CHP-h correspond to the heat production carbon emission coefficient and the power generation carbon emission coefficient of the biomass co-fired waste thermoelectric cogeneration unit respectively; The power constraint conditions include power balance constraints and unit output constraints. The electric power balance constraint is as in (25): P t PV +P t PW +P t buy +P t CHP-p +P t HFC-e +P t bat,dis ≥P t Load +P t Pel +P t eh +P t bat,cha +P t sell (25) Wherein, P t PV and P t PW are the output electric powers of the photovoltaic power generation unit and the wind power generation unit in the t period, P t buy and P t sell represent the electricity purchase amount and the electricity sale amount of the microgrid to the large power grid, P t CHP-p is the output electric power of the combined heat and power unit, is the output power of the hydrogen fuel cell, P t bat_dis and P t bat_cha are the discharge and charge powers of the storage battery respectively; P t Load is the electrical load, P t Pel is the power consumption of the electrolyzer, P t Peh is the power consumption of the electric heating equipment; the thermal power balance constraint is as shown in Equation (26) P t CHP-h +P t Peh-h +P t Tes,dis ≥P t H,Load +P t Tes,cha +P t AC,h (26) Wherein, P t CHP-h represents the heat output of the combined heat and power unit, P t Peh-h represents the heat output of the electric heating equipment, P t Tes-dis and P t Tes-cha respectively represent the injection and release heat powers of the heat storage tank, P t H,Load represents the heat load value, P t AC_h represents the heat consumption of the absorption chiller; the cooling power balance constraint is as shown in Equation (27): P t AC,c = P t C,Load (27) where, P t AC,c represents the refrigeration power of the absorption chiller at time t, and P t C,Load represents the cooling load at time t; Constraints for biomass co-firing waste thermoelectric cogeneration unit (dry waste and biomass maintain a fixed ratio < 20%) Wherein, and are the upper and lower limits of the amount of dry waste that the energy conversion unit can process in the t-th period, and are the upper and lower limits of the biomass fuel burned by the energy conversion unit, and are the upper and lower limits of the electric power output of the energy conversion unit, and are the upper and lower limits of the thermal power output of the energy conversion unit, and are the upper and lower limits of the power ramp of the electric power output of the energy conversion unit, and are the upper and lower limits of the ramp of the heat output of the energy conversion unit, P t CHP-h ≤P t CHP-p ·(λ h / λ p ) restricts the thermal power of the energy conversion unit to be less than the maximum power that can be produced at the current moment; the electrolytic hydrogen and hydrogen fuel cell model constraints are as shown in Equation (29): In the formula, and respectively represent the upper and lower limits of the input electric power of the electrolyzer hydrogen production equipment, and represent the ramp constraint of hydrogen production by the electrolyzer, and represent the ramp constraint of power-to-hydrogen, and respectively represent the upper and lower limits of the output electric power of the hydrogen fuel cell, and represent the ramp constraint of the electric power output of the fuel cell, and respectively represent the upper and lower limits of the hydrogen-to-electricity conversion efficiency value of the hydrogen fuel cell, the output constraint of the battery, the output constraint of the heat storage tank, and the hydrogen storage tank constraint as shown in Equation (30): and respectively represent the upper and lower limits of the charge and discharge power of the storage battery, represents the state of charge of the storage battery at time t, and represent the upper and lower limits of the state of charge of the storage battery; the output constraint of the heat storage tank is as shown in Equation (31) and represent the upper and lower limits of the heat release power of the heat storage tank, and represent the upper and lower limits of the heat charging power of the heat storage tank, and represent the upper and lower limits of the capacity of the heat storage tank; the output constraint of the hydrogen storage tank is as shown in Equation (32) and represent the upper and lower limits of the hydrogen storage tank, and represent the upper and lower limits of the charging and discharging power of the hydrogen storage tank respectively; represent the hydrogen storage tank capacity, hydrogen output power, and hydrogen injection power at time t respectively; The output power constraint of the electric heating device is as shown in Equation (33): Wherein, P t eh,h and P t eh,e respectively represent the heat production and power consumption of the electric heating equipment at time t, and η eh i.e., the electro-thermal conversion ratio of the electric heating equipment, which takes a fixed value here; and are the upper and lower limits of the power consumption of the electric heating equipment, and are the upper and lower limits of the heat production of the electrolysis equipment; The constraints of the absorption chiller are as shown in Equation (34): Wherein, P t AC,c represents the cooling capacity of the absorption chiller at time t, and P t AC,h represents the heat absorption of the chiller at time t, and η AC represents the heat-to-cooling conversion rate of the absorption chiller. The power consumption of this unit is uniformly incorporated into the load curve without separately setting a power conversion formula; and represent the upper and lower limits of the cooling capacity of the absorption chiller. The large power grid's power purchase and sale constraints are as shown in Equation (35): Wherein, and are respectively the upper and lower limits of the electricity purchase amount from the large power grid and the electricity sales amount to the large power grid at time t; The experimental data in this paper comes from the measured data of a microgrid project in a certain park in Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, from July 2023 to July 2024; the time-of-use electricity price is adopted for the purchase and sale of electricity from the large power grid, and the natural gas selling price is a fixed price of 0.35 yuan / (kW·h); the scheduling accuracy is 1 h; the number of iterations of the game cross-efficiency evaluation model is set to 30 times; (4) Solving and improvement The output data of each unit and the game cross-efficiency value are obtained by solving, and the original objective function is improved and looped again until the loop ends; the commercial solver Gurobi is used for solving to obtain the output data of each unit, and the scheduling result is saved; it is judged whether the number of loops is exceeded. If not, the input-output index and the game cross-efficiency value are calculated continuously. All unit efficiency values are obtained by judging whether the efficiency value converges. The original objective function is improved according to the weight coefficient and looped again for the objective function until the number of loops is exceeded, and the optimal scheduling strategy is output to update the objective function; if it has exceeded, the optimal scheduling strategy is directly output.

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