Comprehensive energy system optimization method and system considering cost and new energy consumption

Abstract

A kind of comprehensive energy system optimization method and system giving consideration to cost and new energy consumption, comprising: comprehensively considering the characteristics of multi-energy equipment and spot market electricity price mechanism, constructing regional comprehensive energy system model;Based on the regional comprehensive energy system model, a multi-objective optimization scheduling model containing day-ahead, intra-day, real-time multi-time scale and minimizing system total cost and maximizing new energy consumption is established;The day-ahead and intra-day multi-objective optimization scheduling model is solved;Based on the day-ahead and intra-day optimization results, the real-time multi-objective optimization scheduling model is solved by designing a distributed intelligent agent architecture and using a distributed model predictive control algorithm;According to the solving result, the output plan of each unit, the charging and discharging plan of energy storage and the system energy purchase plan are determined to realize optimal scheduling.The present application comprehensively considers operation cost and new energy consumption, and realizes economic and low-carbon operation of regional comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system operation optimization, and in particular relates to an integrated energy system optimization method and system that takes into account both cost and new energy consumption. Background Technology

[0002] Currently, the energy structure transformation is accelerating, with new energy sources, represented by wind power and photovoltaics, being massively integrated into Regional Integrated Energy Systems (RIES). As the proportion of new energy in the energy structure continues to increase, how to improve the capacity for new energy absorption while ensuring the coordinated operation of the electricity-gas-heat integrated energy system has become a highly challenging and practically significant issue. The coupling of multiple energy types within a Regional Integrated Energy System exhibits multi-energy complementarity, providing a solution to the increased uncertainty caused by the integration of new energy.

[0003] However, the output of new energy sources such as wind power and photovoltaic power is highly intermittent and volatile. The predicted curves often have large errors with the actual output, and the prediction accuracy changes with the time scale. This leads to increased difficulty in matching system sources and loads and increased net load fluctuations. This not only raises the adjustment costs of traditional units, but also causes a large amount of wind and solar curtailment, which seriously restricts the efficient consumption of new energy sources.

[0004] Chinese patent application CN118214001A discloses a multi-timescale power grid energy dispatching method and system. The method includes: day-ahead forecasting of distributed energy resources, constructing a day-ahead planning model with the lowest comprehensive operating cost and the highest non-grid energy utilization rate as multiple objective functions, and solving for the day-ahead dispatching scheme; intraday forecasting of distributed energy resources, constructing an intraday rolling model based on the day-ahead dispatching scheme with the lowest power adjustment cost and the lowest power fluctuation as objective functions, and solving for the intraday dispatching scheme at preset time intervals; and real-time forecasting of distributed energy resources, modifying the intraday dispatching scheme at preset time intervals with the lowest power adjustment deviation as the objective function at each set sampling step size within each preset time interval. However, the patent still has the following shortcomings: (1) Limited equipment scheduling scope: It only considers the scheduling problem of power equipment and does not involve multi-energy coupling equipment; (2) Insufficient multi-objective optimization processing: In the multi-objective optimization scenario, the conflict between objectives is not considered; (3) Lack of market mechanism: The impact of the electricity spot market mechanism on power scheduling is not included, and the impact of market fluctuations cannot be reflected; (4) Single real-time optimization objective: In the real-time stage, only the power adjustment deviation is used as the optimization objective, and real-time cost control and new energy consumption are not considered.

[0005] Chinese patent application CN114362152A discloses a multi-timescale scheduling method for integrated energy systems, including: firstly, constructing models and constraints of distributed energy units, carbon capture systems, and electricity-to-gas equipment; secondly, establishing a multi-objective optimization scheduling model for integrated energy systems, considering day-ahead scheduling and intraday rolling scheduling, with the goal of minimizing operating costs and carbon emission costs; finally, obtaining the operating parameters of the integrated energy system, and using algorithms to solve the optimal solution of the integrated energy system optimization scheduling model under constraints, and using the optimal solution as the scheduling scheme to carry out multi-energy complementary scheduling of the integrated energy system. However, this patent still has the following shortcomings: (1) Insufficient consideration of time scales: only day-ahead and intraday time scales are considered, and the optimization scheduling of integrated energy systems in the real-time stage is not analyzed, which cannot cope with the short-term fluctuations and real-time deviations of new energy sources; (2) Lack of market mechanism: the impact of the electricity spot market mechanism on the economics of integrated energy systems is not considered, and the optimization results deviate from the actual market operation; (3) Insufficient adaptation of solution method: the optimization model is solved using a general algorithm, without combining the characteristics of the multi-timescale optimization scheduling model for algorithm selection. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention utilizes the multi-energy complementarity characteristics of regional integrated energy systems to solve the problems of increased operating costs and insufficient renewable energy absorption caused by large-scale renewable energy integration. It proposes an integrated energy system optimization method that balances cost and renewable energy absorption. Starting from multiple time scales, it schedules flexible equipment and loads within the regional integrated energy system to achieve the goals of improving renewable energy absorption and reducing system operating costs.

[0007] To achieve the above-mentioned objectives, the present invention specifically adopts the following technical solution.

[0008] This invention discloses a comprehensive energy system optimization method that balances cost and renewable energy consumption, comprising the following steps:

[0009] S1: Considering the characteristics of multi-energy devices and the spot market electricity pricing mechanism, construct a regional integrated energy system model;

[0010] S2: Based on the regional integrated energy system model, establish a multi-objective optimization scheduling model that includes day-ahead, intraday, and real-time time scales, and aims to minimize the total system cost and maximize the consumption of new energy sources.

[0011] S3: Solve the day-ahead and intraday multi-objective optimization scheduling model. Based on the day-ahead and intraday optimization results, design a distributed intelligent agent architecture and use a distributed model predictive control algorithm to solve the real-time multi-objective optimization scheduling model.

[0012] S4: Based on the day-ahead, intraday, and real-time optimization results, determine the output plan of each unit, the energy storage charging and discharging plan, and the system energy purchase plan to achieve optimized scheduling.

[0013] More preferably,

[0014] The regional integrated energy system model includes models of various equipment, an electricity cost model of the regional integrated energy system, and power constraint and start-stop constraint models of each piece of equipment; the equipment includes a natural gas combined heat and power system, electric energy storage, an air source heat pump, an electric chiller, an electric boiler, a wind turbine generator set, a photovoltaic generator set, a thermal storage tank, and an ice storage system;

[0015] The electricity cost model is constructed based on the day-ahead clearing price, day-ahead clearing price, and real-time price of the electricity market, and in conjunction with the power consumption and generation of various equipment within the regional integrated energy system. Specifically:

[0016]

[0017]

[0018]

[0019] In the formula, , , These are respectively daytime electricity cost, intraday electricity cost, and real-time electricity cost; , , These are the start times for day-ahead scheduling, intraday scheduling, and real-time scheduling, respectively. , , These represent the total number of dispatch times for the day-ahead, intraday, and real-time dispatches, respectively. , These are the day-ahead pre-sale purchase price and the day-ahead pre-sale price; , These are the day-ahead clearing purchase price and the day-ahead clearing sale price, respectively. , These are the real-time electricity purchase price and the real-time electricity sales price, respectively. for t Net load of the system during the time period.

[0020] More preferably,

[0021] The aforementioned multi-objective optimization scheduling model, which includes multiple time scales such as day-ahead, intraday, and real-time, and aims to minimize the total system cost and maximize the absorption of new energy sources, has the following specific optimization objective function:

[0022] The objective function of the daytime multi-objective optimization scheduling model is: the minimum value of the sum of daytime equipment operating costs, system operation and maintenance costs, energy purchase costs, wind and solar curtailment costs, and unit start-up and shutdown costs, and the maximum value of the sum of daytime wind power output and photovoltaic power output;

[0023] The objective function of the intraday multi-objective optimization scheduling model is: the minimum value of the sum of intraday equipment operating costs, system maintenance costs, energy purchase costs, wind and solar curtailment costs, unit start-up and shutdown costs, and regulation costs, and the maximum value of the sum of the differences between intraday wind and solar power output and corresponding reduction amounts;

[0024] The objective function of the real-time multi-objective optimization scheduling model is: the minimum summation of real-time equipment operating costs, system maintenance costs, energy purchase costs, wind and solar curtailment costs, unit start-up and shutdown costs, regulation costs, and deviation penalty costs, and the maximum summation of the differences between real-time wind and solar power output and their corresponding reduction amounts.

[0025] More preferably,

[0026] The day-ahead multi-objective optimization scheduling model, wherein the day-ahead unit start-up and shutdown costs are specifically:

[0027]

[0028]

[0029] in, The current unit start-up and shutdown costs, This is the time when the dispatch started a few days ago. This represents the total number of scheduling time slots in the previous day. , , respectively equipment j exist t The start / stop status at any given time, the cost of a single start, and the cost of a single stop. N This refers to the number of units in the system.

[0030] When controlling the start-up and shutdown of each unit, the minimum start-up and shutdown time constraints of the unit must also be considered, specifically:

[0031]

[0032] In the formula, , The units j Minimum continuous running time, minimum continuous downtime; , The units j exist t Duration of operation (on / off) and duration of downtime. For the unitj exist t- Start-stop status at moment 1.

[0033] More preferably,

[0034] The intraday multi-objective optimization scheduling model introduces ramp-up constraints and adjustment magnitude constraints to mitigate short-term fluctuations in new energy sources. The ramp-up constraint is as follows:

[0035]

[0036] In the formula, For equipment j Within the day t Power output during a given time period For equipment j Within the day t Output power during the -1 time period; For equipment j Climbing power;

[0037] The adjustment range constraint is:

[0038]

[0039] In the formula, For equipment j Recently t Power output during a given time period The system allows for intraday adjustment rates.

[0040] More preferably,

[0041] The solution to the multi-objective optimization scheduling model for both day-ahead and intraday periods specifically includes the following steps:

[0042] Linearize the nonlinear terms in the objective function and constraints of the optimization model;

[0043] To solve the linearized optimization model, we first calculate the objective function that minimizes the total system cost, thus obtaining the minimum total system cost value. ;

[0044] Introducing cost tolerance increments The total system cost As a constraint, it is embedded in the constraint conditions of the objective function for maximizing the consumption of new energy.

[0045] Solve the constrained objective function for maximizing renewable energy consumption, and record the numerical combination of total system cost and renewable energy consumption; iteratively adjust the cost tolerance increment. Until the total system cost reaches the preset acceptable maximum value;

[0046] Multiple sets of numerical data are visualized, and a numerical combination curve is generated with the total system cost as the horizontal axis and the renewable energy consumption as the vertical axis. The final scheduling scheme is determined in combination with the actual needs of the system.

[0047] More preferably,

[0048] The process of solving the real-time multi-objective optimization scheduling model using a distributed model predictive control algorithm includes the following steps:

[0049] The integrated energy system is divided into independent intelligent agents according to equipment type, and the state vector of each intelligent agent is initialized.

[0050] Establish a rolling window prediction model and a state space model for each agent, and predict future outputs based on the state space models of each agent;

[0051] Based on future outputs and taking into account the correlation and coupling between agents, the objective function of the real-time multi-objective optimization scheduling model is decomposed into optimization sub-objectives of each agent.

[0052] The optimization sub-objectives of each agent are solved in a distributed and collaborative manner, and the rolling window prediction model is corrected by feedback.

[0053] More preferably,

[0054] The independent intelligent agents specifically include energy supply intelligent agents, energy storage intelligent agents, energy consumption intelligent agents, and grid interaction intelligent agents. The energy supply intelligent agents include natural gas combined heat and power systems, wind turbine generators, and photovoltaic generators; the energy storage intelligent agents include electric energy storage, thermal storage tanks, and ice storage systems; the energy consumption intelligent agents include air source heat pumps, electric chillers, and electric boilers; and the grid interaction intelligent agent is the interface for the system to purchase and sell electricity from the grid.

[0055] More preferably,

[0056] The objective function of the real-time multi-objective optimization scheduling model is decomposed into optimization sub-objectives for each agent, wherein the specific optimization sub-objectives of each agent are as follows:

[0057]

[0058] In the formula, , They are respectively t +1 period j The and the first k The reference value for the output of each agent, i.e. the planned output value for the day; , They are respectively t +1 period j The and the first k Real-time output value of each intelligent agent; , They are respectively t +1 period wind power and solar power day-ahead planned reference absorption values; , They are respectively t +1 period wind power and solar power absorption value; , They are respectively t +1 time period in k Wind power and photovoltaic power absorption values ​​under the influence of individual intelligent agents; , They are respectively t +1 time period in k The planned reference absorption values ​​of wind power and photovoltaic power under the influence of individual intelligent agents; , These are cost weight vectors; , These are the weight vectors for new energy consumption. M This represents the total number of intelligent agents.

[0059] Another aspect of the present invention discloses an integrated energy system optimization system that balances cost and new energy consumption based on the aforementioned method, including a regional integrated energy system model construction module, a multi-time-scale multi-objective optimization scheduling model construction module, a multi-time-scale multi-objective optimization scheduling model solving module, and an optimization scheduling implementation module;

[0060] The regional integrated energy system model construction module considers the characteristics of multi-energy devices and the spot market electricity price mechanism to construct a regional integrated energy system model.

[0061] The module for constructing a multi-timescale, multi-objective optimization scheduling model is based on a regional integrated energy system model. It establishes a multi-objective optimization scheduling model that includes day-ahead, intraday, and real-time timescales, with the objectives of minimizing total system cost and maximizing renewable energy consumption.

[0062] The module for solving multi-timescale multi-objective optimization scheduling models solves the day-ahead and intraday multi-objective optimization scheduling models. Based on the day-ahead and intraday optimization results, a distributed intelligent agent architecture is designed, and a distributed model predictive control algorithm is used to solve the real-time multi-objective optimization scheduling model.

[0063] The optimized scheduling implementation module determines the output plan of each unit, the energy storage charging and discharging plan, and the system energy purchase plan based on the day-ahead, intraday, and real-time optimization results, so as to achieve optimized scheduling.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0065] 1. Taking into account the regional integrated energy system and the electricity spot market pricing mechanism, and coupling market signals such as day-ahead pre-clearing price and real-time forecast price, a multi-time-scale electricity cost model is constructed;

[0066] 2. The multi-objective optimization of minimizing total system cost and maximizing renewable energy consumption is transformed into a single-objective optimization problem. The optimal solution set is generated by combining the incremental constraint method. By quantifying the cost tolerance increment, the consumption priority is dynamically adjusted to maximize the utilization of wind power and photovoltaic resources under the premise of controllable cost.

[0067] 3. Construct a collaborative optimization model with multiple time scales, including day-ahead, intraday, and real-time. In the day-ahead stage, optimize the unit start-up and shutdown plan and renewable energy consumption strategy. In the intraday stage, introduce adjustment costs and ramp-up constraints to smooth out short-term fluctuations in renewable energy. In the real-time stage, dynamically correct the unit output deviation based on the distributed model predictive control method, design a distributed intelligent agent architecture, optimize the local objectives of each intelligent agent in parallel, and shorten the real-time optimization response time by combining a rolling time-domain feedback correction mechanism. Attached Figure Description

[0068] Figure 1 This is a framework diagram of the regional integrated energy system in this invention;

[0069] Figure 2 This is a flowchart of the comprehensive energy system optimization method that takes into account both cost and new energy consumption in this invention;

[0070] Figure 3 This is the distributed model predictive control structure in this invention. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0072] Example 1:

[0073] like Figure 1 The diagram shown is a framework diagram of the regional integrated energy system in this embodiment. Based on this framework, as follows: Figure 2 As shown, this invention discloses a comprehensive energy system optimization method that balances cost and renewable energy consumption, comprising the following steps:

[0074] S1: Construct a regional integrated energy system model by comprehensively considering the characteristics of multi-energy devices and the spot market electricity price mechanism;

[0075] Specifically, S1 includes the following steps:

[0076] S101: Constructing the Equipment Model

[0077] The multi-energy equipment for regional integrated energy systems considered in this invention includes combined heat and power (CCHP) systems, electric energy storage, air source heat pumps, electric chillers, electric boilers, new energy power generation devices, thermal storage tanks, ice storage, etc.

[0078] 1) CCHP unit

[0079] A combined cooling, heating, and power (CCHP) unit is an energy coupling and conversion device in a regional integrated energy system. A complete CCHP system mainly consists of a gas turbine (GT), an absorption chiller (AC), and a waste heat boiler (WHB).

[0080] (1)

[0081] In the formula, , , for t GT power generation, heating capacity, and natural gas input during the specified time period; , for t Heating capacity of waste heat boiler and amount of waste heat recovered during the period; , for t AC cooling power and heat absorption during the time period; , , , , The parameters are GT power generation efficiency, GT loss rate, natural gas calorific value, waste heat recovery efficiency, and AC cooling efficiency.

[0082] 2) Energy storage

[0083] Electric energy storage is typically used to address the spatiotemporal mismatch between generator sets and load demand, thereby meeting the peak shaving and valley filling requirements of the power grid. The dynamic characteristics of electric energy storage are generally represented by its state of charge (SOC), and its physical model is as follows:

[0084] (2)

[0085] In the formula, , They are respectively electrical energy storage t time, t The stored energy at time -1; , They are respectively electrical energy storage t The charging and discharging power at any given moment; , These are the charging and discharging efficiencies of electrical energy storage, respectively. For the self-discharge efficiency of electrical energy storage; For time intervals.

[0086] 3) Air source heat pump

[0087] (3)

[0088] In the formula, , For air source heat pumps in t Heating power and power consumption at any time; The electrothermal conversion coefficient is given.

[0089] 4) Electric refrigeration unit

[0090] (4)

[0091] In the formula, for t Real-time cooling power; This represents the power consumption during cooling. This refers to the energy efficiency ratio of the refrigeration unit.

[0092] 5) Electric boiler

[0093] (5)

[0094] In the formula, for t Heating capacity of electric boilers during specific time periods; for t Power consumption of electric boilers during certain time periods; For electrothermal conversion efficiency; for t Heat loss of electric boilers during certain periods.

[0095] 6) Wind turbine generator sets

[0096] Wind energy, as an economical and clean energy source and a common type of distributed energy, converts the kinetic energy of wind into electrical energy. The relationship between its output and wind speed is shown in the following formula.

[0097] (6)

[0098] In the formula, , , , These are the cut-in wind speed, rated wind speed, cut-out wind speed, and actual wind speed, respectively. for t The wind turbine's power generation capacity at any given time; This refers to the rated installed capacity of the wind turbine.

[0099] When the wind turbine output is 0, the unit is in standby mode when the actual wind speed is lower than the cut-in wind speed. The unit starts when the wind speed is higher than the cut-in speed, so that the rotational speed of the wind turbine corresponds to the actual wind speed in real time. The electromagnetic torque of the generator is adjusted by the control system. After the adjustment of the control system, the unit can output stably even when the wind speed exceeds the rated speed.

[0100] 7) Photovoltaic generator sets

[0101] The electrical energy generated by photovoltaic equipment is:

[0102] (7)

[0103] In the formula, t Indicates time; Indicates in t Photovoltaic power output at all times; Indicates the area of ​​the photovoltaic panel; For photovoltaic panel efficiency; for t The real-time total horizontal radiation intensity at any given moment.

[0104] 8) Thermal storage tank

[0105] A thermal storage device is a device that can store and release heat. The dynamic model of the thermal storage tank is established as follows:

[0106] (8)

[0107] In the formula, , They are respectively t The heat storage and heat release capacity of the thermal storage tank at all times; , These are the heat storage and heat release efficiencies of the thermal storage tank, respectively. , They are respectively t Time and t The heat stored at time -1 For time intervals.

[0108] 9) Ice storage

[0109] An ice storage system includes ice storage devices, refrigeration units, and ice melting equipment.

[0110] (9)

[0111] In the formula, , , These are ice storage systems t Total cooling power, ice-melting cooling power, and electric refrigeration cooling power at any given time; , , These are ice storage systems t Total power consumption at any given time, power consumption for ice making, and power consumption for electric refrigeration; , They are respectively t time, t The amount of cold storage capacity of the ice storage system at time -1; , , , These are the ice storage self-loss coefficient, ice storage ice-making efficiency, ice storage ice-melting efficiency, and electric refrigeration efficiency, respectively. For time intervals.

[0112] S102: Constructing an electricity cost model

[0113] Based on the day-ahead clearing price, day-ahead clearing price, and real-time price of the electricity market, and considering the power consumption and power generation of each device in the regional integrated energy system, a multi-time-scale electricity cost model for the integrated energy system is constructed.

[0114] Calculate the integrated energy system based on the power generation and consumption characteristics of the equipment. t The net electrical load for a given period is expressed as follows:

[0115] (10)

[0116] In the formula, for t Net load of the system during the time period; for t Electricity load of users at specific time points.

[0117] The electricity costs for the day-ahead, intraday, and real-time periods are as follows:

[0118] (11)

[0119] (12)

[0120] (13)

[0121] In the formula, , , These are respectively daytime electricity cost, intraday electricity cost, and real-time electricity cost; , , These are the start times for day-ahead scheduling, intraday scheduling, and real-time scheduling, respectively. , , These represent the total number of dispatch times for the day-ahead, intraday, and real-time dispatches, respectively. , These are the day-ahead pre-clearance electricity purchase price and the day-ahead pre-clearance electricity sales price, respectively. , These are the day-ahead clearing purchase price and the day-ahead clearing sale price, respectively. , These are the real-time electricity purchase price and the real-time electricity sales price, respectively.

[0122] S103: Establish a set of routine operating constraints that consider power balance and equipment characteristics.

[0123] 1) Power balance constraints

[0124] (1) Electric power balance constraint

[0125] (14)

[0126] In the formula, for t Electrical energy that interacts with the external environment during a specific time period in an integrated energy system.

[0127] (2) Thermal power balance constraint

[0128] (15)

[0129] In the formula, for t Thermal energy purchased from external sources by the integrated energy system during certain time periods; This represents the node heat load.

[0130] (3) Cold power balance constraint

[0131] (16)

[0132] In the formula, This is the node's cold load.

[0133] 2) Transmission line power constraints

[0134] Because transmission lines have a maximum capacity limit, power constraints must be followed when regional integrated energy systems interact with external power grids.

[0135] (17)

[0136] In the formula, This represents the upper limit of the power transmission capacity of a power transmission line.

[0137] 3) Equipment constraints

[0138] The power output of each energy unit within the regional integrated energy system must meet its own upper and lower output limits.

[0139] (1) Multi-energy coupling device

[0140] (18)

[0141] In the formula, For CCHP units in t Output value at any given moment ; , These represent the upper and lower limits of the power output of the CCHP unit.

[0142] (2) Power supply equipment

[0143] (19)

[0144] In the formula, The output value of the power supply equipment. ; , These represent the upper and lower limits of the power output of the power supply equipment.

[0145] (3) Electrical equipment

[0146] (20)

[0147] In the formula, The output value of the electrical equipment after energy conversion. ; , These are the upper and lower limits of the power output of the electrical equipment, respectively.

[0148] (4) Energy storage equipment

[0149] (twenty one)

[0150] In the formula, , , These represent the current charging and discharging power and capacity of the energy storage device, respectively. , , ; , These are the upper limits for the charging and discharging power of energy storage devices, respectively. , These represent the upper and lower limits of the capacity of energy storage devices, respectively. , These are 0-1 variables representing the charging and discharging states of the energy storage, respectively.

[0151] S2: Based on the regional integrated energy system model, establish a multi-objective optimization scheduling model that includes day-ahead, intraday, and real-time time scales, and aims to minimize the total system cost and maximize the consumption of new energy sources.

[0152] Specifically, S2 includes the following steps:

[0153] S201: Constructing a Day-ahead Optimization Model

[0154] 1) Establish the current day-to-day optimization objective function

[0155] In the day-ahead phase, it is necessary to take into account both system operating costs and renewable energy consumption, and establish a day-ahead optimization objective function that minimizes the total system cost and maximizes renewable energy consumption.

[0156] (1) Minimize the total system cost

[0157] (twenty two)

[0158] In the formula, , , , , These are the day-ahead operating costs of equipment, system maintenance costs, energy purchase costs, wind and solar curtailment costs, and start-up and shutdown costs. The specific calculation formulas are as follows:

[0159] (twenty three)

[0160] (twenty four)

[0161] (25)

[0162] (26)

[0163] (27)

[0164] (28)

[0165] In the formula, For the CCHP system i The unit operating cost of the CCHP system is as follows: It comprises three pieces of equipment: a gas turbine, an absorption chiller, and a waste heat boiler. , , , , , , , These are the unit operating costs for electric energy storage, air source heat pumps, electric chillers, electric boilers, fans, photovoltaic equipment, thermal storage tanks, and ice storage, respectively. For the CCHP system i Unit maintenance cost of each device , , , , , , , These are the unit maintenance costs for electric energy storage, air source heat pumps, electric chillers, electric boilers, fans, photovoltaic equipment, thermal storage tanks, and ice storage. , , , These are the day-ahead natural gas price, the volume of natural gas purchased from the integrated energy system, the heat energy price, and the purchased heat energy; , , , These are respectively the cost of wind curtailment penalty, the cost of solar curtailment penalty, the day-ahead wind power winning bid volume, and the day-ahead solar power winning bid volume; , , respectively equipment j exist t The start / stop status at any time, the cost of a single start, and the cost of a single shutdown; For the CCHP system i One device t The day before the period of effort, , , , , , , , , , They are respectively from the day before t The power of time-of-use electric energy storage charging, electric energy storage discharging, air heat pump heating, electric refrigeration machine cooling, electric boiler heating, fan output, photovoltaic equipment output, thermal storage tank charging, thermal storage tank discharging, and ice storage cooling.

[0166] (2) Maximize the consumption of new energy sources

[0167] (29)

[0168] 2) Day-ahead constraints

[0169] The current optimization model's constraints need to include the set of normal operating constraints described in S103. In addition, the minimum start-up and shutdown time constraints of the unit also need to be considered.

[0170] (30)

[0171] In the formula, , The unitsj Minimum continuous running time, minimum continuous downtime; , The units are respectively in t The duration of the service being powered on and the duration of the service being powered off.

[0172] S202: Constructing an Intraday Optimization Model

[0173] 1) Establish the intraday optimization objective function

[0174] During the intraday phase, the total system cost and renewable energy consumption are also considered. Based on the day-ahead total system cost, adjustment costs are introduced to establish an intraday objective function.

[0175] (1) Minimize the total system cost

[0176] (31)

[0177] In the formula, , , , , , These are the daily operating costs of equipment, system maintenance costs, energy purchase costs, wind and solar curtailment costs, start-up and shutdown costs, and regulation costs.

[0178] The calculation methods for intraday equipment operating costs, system maintenance costs, energy purchase costs, and start-up and shutdown costs are the same as those for daytime. Only the output power of each unit needs to be updated, the daytime electricity price replaced with the intraday electricity price, and the natural gas price replaced with the intraday natural gas price. Further details on these cost items will not be elaborated upon here; only the newly added cost items and the cost of wind and solar curtailment will be calculated and represented.

[0179] (32)

[0180] (33)

[0181] In the formula, , These represent the daily reduction in wind power and the reduction in photovoltaic power, respectively. , , respectively equipment j Intraday unit adjustment cost, intraday power output, and day-ahead power output. The total adjustment cost for the nine devices within the day. Costs of curtailing wind and solar power within the day.

[0182] (2) Maximize the consumption of new energy sources

[0183] (34)

[0184] In the formula, , These are the daily power outputs of wind power and solar power, respectively.

[0185] 2) Intraday constraints

[0186] Intraday constraints need to consider all conventional constraints in S103 and update all data in the constraints to intraday timescale data. In addition, since equipment output needs to be adjusted intraday, unit ramp-up constraints and adjustment range constraints also need to be considered.

[0187] (1) Unit ramping constraints

[0188] (35)

[0189] In the formula, , For the devices respectively j Within the day t Time period t Output power during the -1 time period; For equipment j The climbing power.

[0190] (2) Adjustment range constraint

[0191] (36)

[0192] In the formula, To allow for intraday adjustment rates, For equipment j Recently t Output power during a given time period.

[0193] S203: Constructing a Real-Time Optimization Model

[0194] 1) Establish a real-time optimization objective function

[0195] In the real-time phase, the goal remains to minimize the total system cost and maximize the consumption of new energy. Based on the total system cost in the intraday phase, a deviation assessment is introduced, and the impact of deviation costs on the total cost needs to be considered.

[0196] (1) Minimize the total system cost

[0197] (37)

[0198] In the formula, , , , , , , These include real-time equipment operating costs, system maintenance costs, energy purchase costs, wind and solar curtailment costs, start-up and shutdown costs, regulation costs, and deviation penalty costs.

[0199] The calculation methods for real-time equipment operating costs, system maintenance costs, energy purchase costs, wind and solar curtailment costs, start-up and shutdown costs, and regulation costs are the same as those for intraday operations. However, the intraday unit output power, electricity price, natural gas price, and wind and solar power reduction amounts need to be replaced with real-time data. Furthermore, the regulation power for regulation costs is the difference between real-time and intraday output power. Further details on the aforementioned cost items are omitted here; only the newly added cost items are calculated and represented.

[0200] (38)

[0201] In the formula, Penalty cost per unit deviation; For equipment j Real-time output power.

[0202] (2) Maximize the consumption of new energy sources

[0203] (39)

[0204] In the formula, , Real-time wind and solar power absorption capacity; , Real-time wind and solar power output; , This represents the real-time amount of wind and solar power that has been abandoned.

[0205] 2) Real-time constraints

[0206] Real-time constraints require updating the relevant data in the conventional constraint set of S103 to real-time data. During the real-time phase, deviations from the day-ahead plan's output need to be considered, deviation assessment items need to be added, and grid security constraints need to be taken into account.

[0207] (1) Power grid security constraints

[0208] (40)

[0209] (41)

[0210] In the formula, , , Regional integrated energy system t Real-time frequency, rated frequency, and maximum permissible deviation frequency at any given moment; , , They are respectively t Time Node i Real-time voltage, maximum voltage, and minimum voltage.

[0211] (2) Deviation assessment constraints

[0212] (42)

[0213] In the formula, The real-time allowable deviation rate for the regional integrated energy system.

[0214] S3: Solve the day-ahead and intraday multi-objective optimization scheduling model; based on the day-ahead and intraday optimization results, design a distributed intelligent agent architecture and use a distributed model predictive control algorithm to solve the real-time multi-objective optimization scheduling model;

[0215] Specifically, S3 includes the following steps:

[0216] S301: Solution Method for Day-ahead and Intraday Optimization Models Based on Mixed Integer Linear Programming Algorithm

[0217] Both the current and intraday optimizations have two objectives: minimizing the total system cost and maximizing the absorption of new energy sources. The final optimization scheme is selected based on decision-making preferences.

[0218] 1) Solution of the current optimization model

[0219] Step 1: Determine equipment output parameters, cost coefficients, electricity / gas / heat purchase prices, and day-ahead forecast data for load and renewable energy output; then linearize the nonlinear terms in the objective function of the day-ahead optimization model in the following manner, as follows:

[0220] The cost of wind and solar power curtailment in the objective function of the optimization model is currently considered a non-linear function. To linearize this, an auxiliary variable is introduced. , Let these represent the amounts of wind and solar power curtailment, respectively, and add the following constraints.

[0221] (43)

[0222] In the formula, , These represent the amount of wind and solar power curtailed on the previous day.

[0223] The cost term for wind and solar power curtailment in the objective function of the recently optimized model has been linearized to:

[0224] (44)

[0225] Step 2: Based on all linearized objective functions and constraint sets. First, solve the first optimization objective function (minimize the total system cost) to obtain the minimum total system cost value and the corresponding equipment scheduling scheme.

[0226] Step 3: Transform the bi-objective optimization into a single-objective optimization. This invention employs the incremental constraint method, which fixes one objective and transforms it into a constraint for solving another optimization objective. The minimum total system cost obtained from the solution is added as a constraint for the second optimization objective (maximizing renewable energy consumption), as follows:

[0227] (45)

[0228] In the formula, This represents the total system cost. This represents the minimum total system cost. This is the cost tolerance increment (starting from 0 and increasing).

[0229] Step 4: Solve for the second optimization objective (maximizing renewable energy consumption) to find the maximum renewable energy consumption under the total cost tolerance of the system. f 2. Records ( f 1, f 2) Combine numerical values ​​and repeatedly adjust the cost tolerance increment until... f 1. Reaching the acceptable maximum value.

[0230] Step 5: Calculate the multiple sets of results ( f 1, f 2) Numerical solution visualization: the horizontal axis represents the cost item, and the vertical axis represents the new energy consumption item, forming a numerical combination curve. Combined with the actual needs of the regional integrated energy system, the following is determined: f 1, f 2) Scheduling scheme under numerical combination, namely the daytime start-up and shutdown plan of each unit, the time-period output power of each unit, and the system energy purchase decision.

[0231] 2) Solving the intraday optimization model

[0232] Determine the intraday forecast and actual data, including the output power and upper and lower limits of each device, operation / maintenance cost coefficients, intraday electricity / gas / heat purchase prices, unit ramp-up power, regulation cost coefficients, etc., and solve the intraday optimization model by combining the day-ahead optimization results.

[0233] First, the nonlinear terms in the intraday optimization model are linearized. These nonlinear terms include the adjustment cost term in the objective function and the adjustment magnitude constraints in the constraint set. The specific process is as follows:

[0234] (1) Adjustment cost linearization

[0235] Introducing auxiliary variables To achieve linearization of adjustment costs, additional constraints are added:

[0236] (46)

[0237] In the formula, For the first time in a day j Unit t Adjust the output amount at all times.

[0238] The linearized adjustment cost then becomes:

[0239] (47)

[0240] (2) Adjustment of amplitude constraint linearization

[0241] (48)

[0242] Finally, repeat steps 2 to 5 of the daily optimization model solution to obtain the intraday scheduling strategy, namely the intraday output adjustment plan of each unit, the energy storage charging and discharging strategy, and the energy purchase decision.

[0243] S302: Real-time Optimization Model Solving Method Based on Distributed Model Predictive Control (DMPC)

[0244] like Figure 3 The diagram shows the control structure of a distributed predictive model. It updates optimization information in real time through a "predictive input - rolling optimization - feedback correction" process, solving the real-time optimization model to obtain the real-time output plan of the regional integrated energy system. The solution process for the real-time optimization model based on DMPC is as follows:

[0245] 1) Nonlinear conditional linearization

[0246] The nonlinear terms in the real-time optimization model include: deviation penalty cost, grid frequency constraint, and deviation assessment constraint; the linearized result of the deviation penalty cost is as follows:

[0247] (49)

[0248] (50)

[0249] In the formula, The cost of the deviation penalty after linearization. The auxiliary variable introduced represents the first... j Unit t The output can be adjusted in real time.

[0250] The results of the power grid frequency constraint linearization are as follows: (51)

[0252] The linearization results of the deviation assessment constraints are as follows:

[0253] (52)

[0254] 2) System agent partitioning and initialization

[0255] The integrated energy system is divided into independent intelligent agents according to equipment type, including: energy supply intelligent agents (photovoltaic, wind power, and natural gas combined heat and power), energy storage intelligent agents (electric energy storage, thermal storage tanks, and ice storage), and energy consumption intelligent agents (air source heat pumps, electric chillers, and electric boilers). A grid interaction intelligent agent (electricity purchase / sale interface) is established at the system level to aggregate the output and energy storage power data of the equipment-level intelligent agents, and to actively purchase / sell electricity. When information cannot be transmitted between intelligent agents, autonomous electricity purchase / sale can be carried out based on historical electricity purchase / sale strategies to avoid unstrategic electricity purchase / sale, which would cause costs to surge.

[0256] Based on the real-time power output measurement data of the equipment, the power interaction between the integrated energy system and the external environment, and the real-time electricity price in the spot market, initialize the agent's state vector:

[0257] (53)

[0258] In the formula, For the first j A smart agent t The initial state at time 1. For the first j A smart agent t Real-time output value , as well as The first i Energy storage device t Real-time energy storage power, energy release power, and real-time capacity. for t The power of the integrated energy system interacting with the external environment at all times. , They are respectively t Real-time electricity purchase and sales prices in the spot market.

[0259] 3) Establish a rolling window prediction model

[0260] The model contains 10 agents; the rolling time domain length is defined as... T (Typically 5 to 15 control cycles, corresponding to 5 to 15 minutes), the control time domain is 1, that is, only the control quantity at the current moment is executed.

[0261] Predicting future outputs based on state-space models:

[0262] (54)

[0263] In the formula, For the first j An intelligent agent in t The state vector at time +1; For the first j An intelligent agent in t The control vector at any given time represents the equipment's output command; For the first k ( k ≠ j ) intelligent agents in t The state vector of a time period; For the first k ( k ≠ j ) intelligent agents in t Control vector for a given time period; , , The first j The state transition matrix, input-state coefficient matrix, and state-output coefficient matrix of each agent; , , The first j The first agent and the second k State coupling matrix between agents, input-state coupling matrix, input-output coupling matrix; For the first j An intelligent agent in t The output vector for the +1 time interval is represented as follows:

[0264] (55)

[0265] In the formula, , , , , , , , , The first j An intelligent agent in t +1 moment: Gas turbine power generation, gas turbine heating power, waste heat boiler heating power, absorption chiller cooling power, wind turbine power generation, photovoltaic unit power generation, air source heat pump heating power, electric chiller cooling power, electric boiler heating power; , , , , , , The first j An intelligent agent in t +1 time: Electric energy storage charging power, thermal storage tank heat storage power, ice-making power of ice storage system, electric energy storage discharge power, thermal storage tank heat release power, ice melting and cooling power of ice storage system, and power interaction between the system and the power grid.

[0266] 4) Construct a single-agent optimization model

[0267] (1) Objective function

[0268] The objective function of the real-time optimization phase is transformed into the optimization objective function of each agent, while also taking into account the correlation and coupling between the agents. j The optimization objective for each agent is:

[0269] (56)

[0270] In the formula, , They are respectively t +1 period j The and the first k The reference value for the output of each agent, i.e. the planned output value for the day; , They are respectively t +1 period j The and the first k Real-time output value of each intelligent agent; , They are respectively t +1 period wind power and solar power day-ahead planned reference absorption values; , They are respectively t +1 period wind power and solar power absorption value; , They are respectively t +1 time period in k Wind power and photovoltaic power absorption values ​​under the influence of individual intelligent agents; , They are respectively t +1 time period in k The planned reference absorption values ​​of wind power and photovoltaic power under the influence of individual intelligent agents; , These are cost weight vectors; , These are the weight vectors for new energy consumption.

[0271] (2) Constraints

[0272] The constraints need to consider both the agent's own constraints and global optimization constraints. The agent's own constraints are as follows:

[0273] Power upper and lower limits:

[0274] (57)

[0275] In the formula, , The first j A smart agent t The upper and lower limits of the control quantity at any given time.

[0276] Climbing speed:

[0277] (58)

[0278] In the formula, for t +1 moment j The state vector of each agent. , The respective j An intelligent agent in t The upper and lower limits of the control quantity increment at any given time.

[0279] State constraints:

[0280] (59)

[0281] In the formula, , They are respectively t +1 moment j Upper and lower limits of the output of an agent.

[0282] The global optimization constraints are the same as those of the real-time optimization model, so we will not go into details here.

[0283] 5) Distributed collaborative solution and feedback correction

[0284] Step 1: Based on the intraday adjustment plan, initialize the control variables in the forecasting model. ;

[0285] Step 2: Predict the output vector value of the computer group according to equation (54). ;

[0286] Step 3: Each agent solves the local problem and obtains the optimal solution by assuming that the optimal solutions of other agents are known. ;

[0287] Step 4: Stop iterating if the following formula is satisfied:

[0288] (60)

[0289] In the formula, ε This is the set precision threshold.

[0290] Otherwise, update the optimal solution and repeat:

[0291] (61)

[0292] In the formula, To update the weight coefficients, which are used to balance the ratio of new solutions to old solutions during algorithm iterations, The initial value is set to 0.6. The parameter is then modified according to the convergence speed of the algorithm. If the convergence is fast, the parameter value is increased until the parameter is 1, which means the new solution is fully adopted. If the convergence is slow, the parameter value is decreased until the parameter is 0, which means the new solution is not adopted.

[0293] Step 5: Only execute the plan for the first phase. Collect actual unit output and load data of the integrated energy system after the plan is executed, and revise the prediction model accordingly;

[0294] Step 6: Move the time forward Then, start executing from Step 1 again to form a feedback loop optimization.

[0295] S4: Based on the day-ahead, intraday, and real-time optimization results, determine the output plan of each unit, the energy storage charging and discharging plan, and the system energy purchase plan to achieve optimized scheduling.

[0296] This invention also claims protection for a comprehensive energy system optimization system based on the aforementioned method that balances cost and renewable energy consumption, including a regional comprehensive energy system model construction module, a multi-timescale multi-objective optimization scheduling model construction module, a multi-timescale multi-objective optimization scheduling model solving module, and an optimization scheduling implementation module;

[0297] The regional integrated energy system model construction module comprehensively considers the characteristics of multi-energy devices and the spot market electricity price mechanism to construct a regional integrated energy system model;

[0298] The module for constructing a multi-timescale, multi-objective optimization scheduling model is based on a regional integrated energy system model. It establishes a multi-objective optimization scheduling model that includes day-ahead, intraday, and real-time timescales, with the objectives of minimizing total system cost and maximizing renewable energy consumption.

[0299] The module for solving multi-timescale multi-objective optimization scheduling models solves the day-ahead and intraday multi-objective optimization scheduling models. Based on the day-ahead and intraday optimization results, a distributed intelligent agent architecture is designed, and a distributed model predictive control algorithm is used to solve the real-time multi-objective optimization scheduling model.

[0300] The optimized scheduling implementation module determines the output plan of each unit, the energy storage charging and discharging plan, and the system energy purchase plan based on the day-ahead, intraday, and real-time optimization results, so as to achieve optimized scheduling.

[0301] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0302] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0303] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0304] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0305] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A comprehensive energy system optimization method that balances cost and renewable energy consumption, characterized in that, Includes the following steps: S1: Considering the characteristics of multi-energy devices and the spot market electricity pricing mechanism, construct a regional integrated energy system model; The regional integrated energy system model includes models of various equipment, an electricity cost model of the regional integrated energy system, and power constraint and start-stop constraint models of each piece of equipment; the equipment includes a natural gas combined heat and power system, electric energy storage, an air source heat pump, an electric chiller, an electric boiler, a wind turbine generator set, a photovoltaic generator set, a thermal storage tank, and an ice storage system; The electricity cost model is constructed based on the day-ahead clearing price, day-ahead clearing price, and real-time price of the electricity market, and in conjunction with the power consumption and generation of various equipment within the regional integrated energy system. Specifically: In the formula, , , These are respectively daytime electricity cost, intraday electricity cost, and real-time electricity cost; , , These are the start times for day-ahead scheduling, intraday scheduling, and real-time scheduling, respectively. , , These represent the total number of dispatch times for the day-ahead, intraday, and real-time dispatches, respectively. , These are the day-ahead pre-sale purchase price and the day-ahead pre-sale price; , These are the day-ahead clearing purchase price and the day-ahead clearing sale price, respectively. , These are the real-time electricity purchase price and the real-time electricity sales price, respectively. for t Net load of the system during the time period; S2: Based on the regional integrated energy system model, establish a multi-objective optimization scheduling model that includes day-ahead, intraday, and real-time time scales, and aims to minimize the total system cost and maximize the consumption of new energy sources. S3: Solve the day-ahead and intraday multi-objective optimization scheduling model. Based on the day-ahead and intraday optimization results, design a distributed intelligent agent architecture and use a distributed model predictive control algorithm to solve the real-time multi-objective optimization scheduling model. S4: Based on the day-ahead, intraday, and real-time optimization results, determine the output plan of each unit, the energy storage charging and discharging plan, and the system energy purchase plan to achieve optimized scheduling.

2. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 1, characterized in that: The aforementioned multi-objective optimization scheduling model, which includes multiple time scales such as day-ahead, intraday, and real-time, and aims to minimize the total system cost and maximize the absorption of new energy sources, has the following specific optimization objective function: The objective function of the daytime multi-objective optimization scheduling model is: the minimum value of the sum of daytime equipment operating costs, system operation and maintenance costs, energy purchase costs, wind and solar curtailment costs, and unit start-up and shutdown costs, and the maximum value of the sum of daytime wind power output and photovoltaic power output; The objective function of the intraday multi-objective optimization scheduling model is: the minimum value of the sum of intraday equipment operating costs, system maintenance costs, energy purchase costs, wind and solar curtailment costs, unit start-up and shutdown costs, and regulation costs, and the maximum value of the sum of the differences between intraday wind and solar power output and corresponding reduction amounts; The objective function of the real-time multi-objective optimization scheduling model is: the minimum summation of real-time equipment operating costs, system maintenance costs, energy purchase costs, wind and solar curtailment costs, unit start-up and shutdown costs, regulation costs, and deviation penalty costs, and the maximum summation of the differences between real-time wind and solar power output and their corresponding reduction amounts.

3. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 2, characterized in that: The day-ahead multi-objective optimization scheduling model, wherein the day-ahead unit start-up and shutdown costs are specifically: in, The current unit start-up and shutdown costs, This is the time when the dispatch started a few days ago. This represents the total number of scheduling time slots in the previous day. , , respectively equipment j exist t The start / stop status at any given time, the cost of a single start, and the cost of a single stop. N This refers to the number of units in the system. When controlling the start-up and shutdown of each unit, the minimum start-up and shutdown time constraints of the unit must also be considered, specifically: In the formula, , The units j Minimum continuous running time, minimum continuous downtime; , The units j exist t Duration of operation (on / off) and duration of downtime. For the unit j exist t- Start-stop status at moment 1.

4. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 3, characterized in that: The intraday multi-objective optimization scheduling model introduces ramp-up constraints and adjustment magnitude constraints to mitigate short-term fluctuations in new energy sources. The ramp-up constraint is as follows: In the formula, For equipment j Within the day t Power output during a given time period For equipment j Within the day t Output power during the -1 time period; For equipment j Climbing power; The adjustment range constraint is: In the formula, For equipment j Recently t Power output during a given time period The system allows for intraday adjustment rates.

5. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 4, characterized in that: The solution to the multi-objective optimization scheduling model for both day-ahead and intraday periods specifically includes the following steps: Linearize the nonlinear terms in the objective function and constraints of the optimization model; To solve the linearized optimization model, we first calculate the objective function that minimizes the total system cost, thus obtaining the minimum total system cost value. ; Introducing cost tolerance increments The total system cost As a constraint, it is embedded in the constraint conditions of the objective function for maximizing the consumption of new energy. Solve the constrained objective function for maximizing renewable energy consumption, and record the numerical combination of total system cost and renewable energy consumption; iteratively adjust the cost tolerance increment. Until the total system cost reaches the preset acceptable maximum value; Multiple sets of numerical data are visualized, and a numerical combination curve is generated with the total system cost as the horizontal axis and the renewable energy consumption as the vertical axis. The final scheduling scheme is determined in combination with the actual needs of the system.

6. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 5, characterized in that: The process of solving the real-time multi-objective optimization scheduling model using a distributed model predictive control algorithm includes the following steps: The integrated energy system is divided into independent intelligent agents according to equipment type, and the state vector of each intelligent agent is initialized. Establish a rolling window prediction model and a state space model for each agent, and predict future outputs based on the state space models of each agent; Based on future outputs and taking into account the correlation and coupling between agents, the objective function of the real-time multi-objective optimization scheduling model is decomposed into optimization sub-objectives of each agent. The optimization sub-objectives of each agent are solved in a distributed and collaborative manner, and the rolling window prediction model is corrected by feedback.

7. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 6, characterized in that: The independent intelligent agents specifically include energy supply intelligent agents, energy storage intelligent agents, energy consumption intelligent agents, and grid interaction intelligent agents. The energy supply intelligent agents include natural gas combined heat and power systems, wind turbine generators, and photovoltaic generators; the energy storage intelligent agents include electric energy storage, thermal storage tanks, and ice storage systems; the energy consumption intelligent agents include air source heat pumps, electric chillers, and electric boilers; and the grid interaction intelligent agent is the interface for the system to purchase and sell electricity from the grid.

8. The comprehensive energy system optimization method that balances cost and new energy consumption according to claim 6, characterized in that: The objective function of the real-time multi-objective optimization scheduling model is decomposed into optimization sub-objectives for each agent, wherein the specific optimization sub-objectives of each agent are as follows: In the formula, , They are respectively t +1 period j The and the first k The reference value for the output of each agent, i.e. the planned output value for the day; , They are respectively t +1 period j The and the first k Real-time output value of each intelligent agent; , They are respectively t +1 period wind power and solar power day-ahead planned reference absorption values; , They are respectively t +1 period wind power and solar power absorption value; , They are respectively t +1 time period in k Wind power and photovoltaic power absorption values ​​under the influence of individual intelligent agents; , They are respectively t +1 time period in k The planned reference absorption values ​​of wind power and photovoltaic power under the influence of individual intelligent agents; , These are cost weight vectors; , These are the weight vectors for new energy consumption. M This represents the total number of intelligent agents.

9. A comprehensive energy system optimization system based on the method of any one of claims 1-8, balancing cost and new energy consumption, comprising a regional comprehensive energy system model construction module, a multi-timescale multi-objective optimization scheduling model construction module, a multi-timescale multi-objective optimization scheduling model solving module, and an optimization scheduling implementation module, characterized in that: The regional integrated energy system model construction module considers the characteristics of multi-energy devices and the spot market electricity price mechanism to construct a regional integrated energy system model. The module for constructing a multi-timescale, multi-objective optimization scheduling model is based on a regional integrated energy system model. It establishes a multi-objective optimization scheduling model that includes day-ahead, intraday, and real-time timescales, with the objectives of minimizing total system cost and maximizing renewable energy consumption. The module for solving multi-timescale multi-objective optimization scheduling models solves the day-ahead and intraday multi-objective optimization scheduling models. Based on the day-ahead and intraday optimization results, a distributed intelligent agent architecture is designed, and a distributed model predictive control algorithm is used to solve the real-time multi-objective optimization scheduling model. The optimized scheduling implementation module determines the output plan of each unit, the energy storage charging and discharging plan, and the system energy purchase plan based on the day-ahead, intraday, and real-time optimization results, so as to achieve optimized scheduling.

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