Economic efficiency optimal algorithm for multi-source heat supply system in electricity market

Abstract

The invention discloses an economical efficiency optimal algorithm for a multi-source heat supply system in an electricity market. The economical efficiency optimal algorithm comprises a real-time data acquisition module, an economical efficiency evaluation module, an optimization strategy generation module and a control execution module, the real-time data acquisition module is used for monitoring and acquiring the running state, the fuel consumption and the heat supply amount of the cogeneration unit in real time; the economy evaluation module is used for fitting heat supply capacity curves of the cogeneration unit in different operation states based on the data acquired by the real-time data acquisition module; the optimization strategy generation module calculates an optimization value of the cogeneration unit based on the economic profit value in combination with a particle swarm optimization algorithm; the control execution module adjusts and optimizes the operation state of the cogeneration unit based on the optimization value; load fluctuation can be accurately predicted and responded, and the stability and reliability of a heat supply system are improved; accurate regulation and control and optimized operation of the multi-source heat supply system are achieved, the operation cost is remarkably reduced, and the profitability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to an economic optimization algorithm for a multi-source heating system in an electric power market. Background Art

[0002] The electricity market has both broad and narrow meanings. The broad electricity market refers to the sum of electricity production, transmission, use and sales. The narrow electricity market refers to the competitive electricity market, which is a mechanism for electricity producers and users to trade electricity and related products through negotiation, bidding, etc., and determine prices and quantities through market competition. A power plant is a power plant that converts some form of raw energy into electricity for fixed facilities or transportation, such as hydropower, steam, diesel or nuclear power plants. In the electricity market model that introduces a competitive mechanism on the power generation side, the goal of power plant risk management is to eliminate the volatility of its profits. And the managers of power plants must be able to assess the potential losses during the operation of the power plant.

[0003] With the continuous growth of energy demand and increasingly stringent environmental protection requirements, multi-source heating systems have received widespread attention and application as an efficient and environmentally friendly way of energy utilization. However, in actual operation, multi-source heating systems face many challenges, such as load fluctuations, energy price fluctuations, equipment aging, etc. These problems will affect the economy and stability of the heating system. Therefore, how to accurately control and optimize the operation of multi-source heating systems to improve their economy and stability has become a hot topic and difficulty in current research.

[0004] Therefore, there is an urgent need for an optimal economic algorithm for a multi-source heating system in an electricity market to solve the technical problems existing in the above-mentioned prior art. Summary of the invention

[0005] The present invention overcomes the shortcomings of the prior art and provides an optimal algorithm for the economic efficiency of a multi-source heating system in an electricity market.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an optimal economic algorithm for a multi-source heating system in an electricity market, comprising: a real-time data acquisition module, an economic evaluation module, an optimization strategy generation module and a control execution module;

[0007] The real-time data acquisition module is used to monitor and obtain the operating status, fuel consumption and heat supply of the cogeneration unit in real time;

[0008] The economic evaluation module fits the heating capacity curve of the cogeneration unit under different operating conditions based on the data acquired by the real-time data acquisition module, calculates the fuel consumption rate under the real-time state, and calculates the economic profit value based on the fuel consumption rate, combined with the fuel cost and the energy consumption of the cogeneration unit;

[0009] The optimization strategy generation module calculates the optimization value of the cogeneration unit based on the economic profit value and in combination with a particle swarm optimization algorithm;

[0010] The control execution module adjusts and optimizes the operating state of the cogeneration unit based on the optimization value.

[0011] In a preferred embodiment of the present invention, the cogeneration unit includes a gas-fired unit and a coal-fired unit, and the fuel consumption includes the gas consumption of the gas-fired unit under different operating conditions and the coal consumption of the coal-fired unit under different operating conditions; the economic evaluation module calculates the gas consumption rate and coal consumption rate of the cogeneration unit under different operating conditions according to the gas consumption and coal consumption of the cogeneration unit under different operating conditions.

[0012] In a preferred embodiment of the present invention, the calculation expression of the heating capacity curve of the cogeneration unit under different operating conditions is:

[0013]

[0014] In the formula: G1 is the heating capacity of the gas-fired unit under different operating conditions; Q1 is the load of the gas-fired unit under different operating conditions; a1, b1, c1, and d1 are all fitting coefficients. In this algorithm, a1=1.24, b1=0.0043, c1=0.00001, and d1=0.5503; G2 is the heating capacity of the coal-fired unit under different operating conditions; Q2 is the load of the coal-fired unit under different operating conditions; a2, b2, c2, and d2 are all fitting coefficients. In this algorithm, a2=0.66, b2=0.00288, c2=0.0000056, and d2=2.9917.

[0015] In a preferred embodiment of the present invention, the calculation expressions of the gas consumption rate and coal consumption rate of the cogeneration unit under different operating conditions are as follows:

[0016] g qfd =β q *D ng / E qf

[0017] g rfd =β r *D ng / E rf

[0018]

[0019] Where: g qfd is the gas consumption rate of the gas-fired unit in real time, unit: kg / (kW·h); β q D is the cost allocation ratio of gas-fired units for power generation; ng is the natural gas flow rate, unit: kg / h; E qf is the real-time power generation of the steam turbine, unit: MW / h; g rfd is the gas consumption rate of the coal-fired unit in real time, unit: kg / (kW·h); β r E is the cost allocation ratio of coal-fired units; qf The real-time power generation of the gas turbine, unit: MW / h; is the coal consumption rate of coal-fired units; Coal consumption rate for heating coal-fired units; η tp,e is the thermal efficiency of the combined heat and power unit.

[0020] In a preferred embodiment of the present invention, the power generation cost allocation ratio β of the gas unit is q , the cost allocation ratio of coal-fired units β r And the thermal efficiency of the combined heat and power unit η tp,e The calculation expression is:

[0021] β q =Q sr / Q tp =Q sr / D ng LHV

[0022] β h =Q tp(h) / Q tp =Q tp(h) / D ng LHV

[0023] β r =1-β q -β h

[0024]

[0025] Where: β h is the heating cost allocation ratio of the gas unit; Q sr is the heat consumption of the gas unit, unit: KJ / h; Q tp is the total heat consumption of the cogeneration unit; Q tp(h) is the heating consumption of the gas unit, unit: KJ / h; D ng LHV is Q tpThe expression of LHV is the lower heating value of the fuel, unit: KJ / kg; η tp,e P is the thermal efficiency of the combined heat and power unit; e is the real-time power generation of the gas turbine unit, unit: MW / h; Q tp,e Heat consumption to supply electricity to the CHP group.

[0026] In a preferred embodiment of the present invention, the real-time gas consumption rate of heating of the coal-fired unit is g gr The calculation expression is:

[0027] g gr =β h *D ng / Q gr

[0028] In the formula, β h is the heating cost allocation ratio of the gas unit; D ng Q tp expression.

[0029] In a preferred embodiment of the present invention, the heat consumption Q of the gas unit is sr , heating consumption of gas unit Q tp(h) And the total heat consumption Q of the cogeneration unit tp The calculation expression is:

[0030] Q sr =D0*(h0-h c )*d

[0031]

[0032] Q tp,e =Q tp -Q tp,h

[0033]

[0034] Where: D0 is the total steam flow before the gas unit valve, unit: kg / h; h0 is the specific enthalpy of new steam, unit: KJ / kg; h c Q is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg; t is the total heat consumption of the gas unit, unit: KJ / h; D h The steam extraction capacity for heating of gas units, unit: kg / h; h h is the heating extraction enthalpy, unit: KJ / kg; D is the main steam flow, unit: kg / h; h fw is the feed water enthalpy, unit: kJ / kg; Q rh is the amount of reheat steam, unit: kg / h; h rh，0 、h rh，iare the outlet enthalpy and inlet enthalpy of the reheater, respectively, unit: kJ / kg; Q tp,e is the heat consumption of the combined heat and power generation unit; d is the steam consumption rate of the steam turbine, unit: kg / kwh; Q tp，h Q is the heat consumption of coal-fired units, in KJ / h; tp(h) The heat consumption of the gas unit for heating, unit: KJ / h; Q h Supply heat to the cogeneration unit; η b η p It is boiler efficiency and pipeline efficiency.

[0035] In a preferred embodiment of the present invention, the total heat consumption Q of the gas unit is t The heat supply Q of the cogeneration group h The calculation expression is:

[0036] Q t =D gas *(h g -h ex )

[0037] Q h =D h *(h h -h c )

[0038] Where: h g is the flue gas enthalpy at the gas unit outlet, unit: KJ / kg; h ex is the flue gas enthalpy at the outlet of the waste heat boiler, unit: KJ / kg; D gas is the flue gas volume at the gas unit outlet, in kg / h; h h is the heating exhaust enthalpy, unit: KJ / kg; h c D is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg; h It is the steam extraction capacity for heating of gas-fired units, unit: kg / h.

[0039] In a preferred embodiment of the present invention, the fuel cost calculation expression of the cogeneration unit in real time is:

[0040]

[0041] Where: P mi is the real-time power generation of the cogeneration unit i; Q mi Real-time heat supply for i cogeneration unit; K m is the coal price; K fd , K gr are the electricity price for power generation and the gas price for heating respectively; ri is the real-time power generation of coal-fired unit i; Q ri Real-time heat supply for i gas unit; fi is the real-time flow rate of natural gas for No. 1 gas boiler; d mi d ri d li i represents the real-time power consumption of coal-fired units, gas-fired units, and gas-fired boilers; s mi 、s ri 、s li Indicates the real-time water consumption of coal-fired units, gas-fired units and gas boilers.

[0042] In a preferred embodiment of the present invention, the calculation expression of the economic profit value of the cogeneration unit is:

[0043]

[0044] Where: k md k is the electricity selling price of the cogeneration unit; q is the price of steam for heating; k rd is the electricity selling price of the cogeneration unit; Q mi , Q qi and Q li is the real-time theoretical value of heat supply of gas boiler i.

[0045] In a preferred embodiment of the present invention, the economic profit target value L(max) of the cogeneration unit is set in the control execution module; the control execution module receives and optimizes the economic profit value with the economic profit target value as an evaluation criterion, and the calculation expression of the economic profit target value L(max) is:

[0046]

[0047] Where L(max) is the target value of economic profit. The larger its value is, the better the optimization result is, and vice versa. mix and P qix They respectively represent the real-time load correction values ​​of coal-fired unit i and gas-fired unit i after optimized scheduling.

[0048] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0049] (1) The present invention uses a real-time data acquisition and economic evaluation module to monitor and obtain the operating status and fuel consumption of the cogeneration unit in real time, and fit the heating capacity curve under different operating conditions, which helps to accurately predict and respond to load fluctuations and improve the stability and reliability of the heating system; at the same time, it also realizes the precise control and optimized operation of the multi-source heating system, significantly reduces the operating cost and improves profitability.

[0050] (2) The present invention enables the heating system to better adapt to the demand fluctuations in the electricity market by fitting the heating capacity curve and adjusting the operating status in real time according to the heating performance of the cogeneration unit under different load conditions, thereby improving the flexibility and responsiveness of the system. By optimizing the strategy generation and control execution module, the economic optimal heating strategy of the cogeneration unit can be formulated based on the economic evaluation results and the particle swarm optimization algorithm, thus achieving the optimal configuration and efficient utilization of energy, and reducing the operating cost and environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a scheduling method framework of a multi-source heating collaborative control system based on electric load instructions of the present invention;

[0053] Figure 2 It is a fitting diagram of the experimental data of the gas unit load and heating capacity of the present invention;

[0054] Figure 3 It is a fitting diagram of the experimental data of the load and heating capacity of the coal-fired unit of the present invention;

[0055] Figure 4 It is the comprehensive economic iteration curve of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0058] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the scope of protection of the present application. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the invention, unless otherwise specified, "multiple" means two or more.

[0059] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0060] In the multi-source heating system that this study focuses on, the AGC system controls the following equipment:

[0061] like Figure 1 As shown, Units 1 and 2 coal-fired cycle generating units: These units are directly controlled by the electrical load commands of the AGC system to respond to real-time changes in grid demand.

[0062] Gas turbine section of Units 1 and 2 gas-fired combined cycle generators: Similar to the coal-fired units, these gas turbines are also controlled by the load command of the AGC system to ensure that the power generation output is synchronized with the grid demand.

[0063] Steam turbine section of the No. 1 and No. 2 gas combined cycle generator units: Unlike the above-mentioned gas turbines, the electrical load of the steam turbine is not controlled by AGC, providing flexibility in system operation to adapt to changes in heating demand.

[0064] Since the heating capacity of each unit is closely related to its load, the real-time load control changes of the AGC system directly lead to corresponding changes in the heating capacity of each unit. This control mechanism ensures that the heating system can flexibly adapt to the demand fluctuations in the power market.

[0065] An economic optimization algorithm for a multi-source heating system in an electricity market comprises: a real-time data acquisition module, an economic evaluation module, an optimization strategy generation module and a control execution module.

[0066] The real-time data acquisition module is used to monitor and obtain the operating status, fuel consumption and heating capacity of the cogeneration unit in real time; through real-time data acquisition, economic evaluation, optimization strategy generation and control execution modules, this technical solution realizes the precise regulation and optimization of the multi-source heating system. The comprehensive economic performance of the optimized heating system has increased by 1.21%, significantly reducing operating costs and improving profitability.

[0067] Based on the data obtained by the real-time data acquisition module, the economic evaluation module fits the heating capacity curve of the cogeneration unit under different operating conditions, calculates the fuel consumption rate under the real-time state, and calculates the economic profit value based on the fuel consumption rate, combined with the fuel cost and the energy consumption of the cogeneration unit; by fitting the heating capacity curve and adjusting the operating status in real time, the heating system can better adapt to the demand fluctuations in the electricity market, thereby improving the flexibility and responsiveness of the system.

[0068] The optimization strategy generation module calculates the optimization value of the cogeneration unit based on the economic profit value and the particle swarm optimization algorithm; through the particle swarm optimization algorithm, the economic optimal heating strategy of each unit can be formulated to achieve the optimal allocation of resources. This not only helps to improve the heating efficiency, but also reduces energy waste and environmental pollution.

[0069] The control execution module adjusts and optimizes the operating status of the cogeneration unit based on the optimization value. Decisions are made based on real-time data and scientific algorithms, providing a scientific basis and decision support for the operation and management of the heating system. This helps reduce errors and risks caused by human factors and improves the stability and reliability of the heating system.

[0070] In a preferred embodiment, the cogeneration unit includes a gas-fired unit and a coal-fired unit, and the fuel consumption includes the gas consumption of the gas-fired unit under different operating conditions and the coal consumption of the coal-fired unit under different operating conditions; the economic evaluation module calculates the gas consumption rate and coal consumption rate of the cogeneration unit under different operating conditions based on the gas consumption and coal consumption of the cogeneration unit under different operating conditions.

[0071] Embodiment 1

[0072] This example aims to explore the heating performance of cogeneration units and combined heat and power units under different load conditions, and to provide a scientific basis for achieving precise control and optimized operation of the heating system. First, the cogeneration units were tested under different load conditions, and the experimental data of load Q (MW) and heating capacity G (t / h) were collected, as shown in Table 1. During the experiment, the consistency of the test conditions and the accuracy of the data were ensured, providing a reliable foundation for subsequent data processing and algorithm model establishment.

[0073] Table 1 Experimental data of heating capacity of gas unit load Q and heating supply G under different loads

[0074]

[0075]

[0076] The above experimental data were processed using MATLAB software. Through the function toolbox, a cubic polynomial fitting was performed on the relationship between the load Q (MW) of the cogeneration unit and the heating capacity G (t / h), and the fitting formula for the heating capacity of the cogeneration unit under different loads was obtained:

[0077]

[0078] Wherein: fitting coefficient a1=1.24, b1=0.0043, c1=0.00001, d1=0.5503; G1 is the heating capacity of the gas unit under different operating conditions; Q1 is the load of the gas unit under different operating conditions.

[0079] The heating capacity curve of the cogeneration unit under different operating conditions is as follows: Figure 2 As shown in Figure 2, it is used to represent the experimental data fitting of gas unit load and heating capacity. It can be seen that the fitting result of the function is good, and the goodness of fit R2 can reach 0.946.

[0080] Secondly, the cogeneration unit was tested under different load conditions, and the experimental data of load Q (MW) and heating capacity G (t / h) are shown in Table 2.

[0081] Table 2 Experimental data of heating capacity of coal-fired units under different loads, load Q and heating supply G

[0082]

[0083]

[0084] Using the above method, a polynomial fitting was also performed on the functional relationship between the load Q (MW) of the cogeneration unit and the heating capacity G (t / h), as shown in the following figure: Figure 1-2As shown. The fitting formula for the heating capacity of the cogeneration unit under different loads is:

[0085]

[0086] In the formula: fitting coefficient a2=0.66, b2=0.00288, c2=0.0000056, d2=2.9917; G2 is the heating capacity of the coal-fired unit under different operating conditions; Q2 is the load of the coal-fired unit under different operating conditions.

[0087] The heating capacity curve of the cogeneration unit under different operating conditions is as follows: Figure 3 As shown in Figure 2, it is used to represent the experimental data fitting of coal-fired unit load and heating capacity. The goodness of fit R2 of the fitting curve is 0.912.

[0088] Through comparative analysis, it can be found that the function can accurately describe the changing trend of the heating supply of the cogeneration unit in the low-load area and the high-load area, and the model prediction is highly consistent with the experimental data.

[0089] The present invention uses real-time data to calculate the gas consumption rate and coal consumption rate in the power generation and heating processes respectively. The gas consumption rate is defined as the ratio of the total amount of natural gas consumed in the heating process to the total amount of thermal energy provided, while the coal consumption rate is defined as the ratio of the total amount of coal consumed in the power generation process to the total amount of electrical energy generated.

[0090] First, for the total heat consumption Q of the gas unit t (kJ / h) is calculated using the formula:

[0091] Q t =D gas *(h g -h ex )

[0092] Where: h g is the flue gas enthalpy at the gas unit outlet, unit: KJ / kg; h ex is the flue gas enthalpy at the outlet of the waste heat boiler, unit: KJ / kg; D gas It is the flue gas volume at the gas unit outlet, in kg / h.

[0093] Its heating heat consumption Q tp(h) (kJ / h) is calculated using the formula:

[0094]

[0095] Where: D0 is the total steam flow before the gas unit valve, unit: kg / h; h0 is the specific enthalpy of new steam, unit: KJ / kg; h c Q is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg; tis the total heat consumption of the gas unit, unit: KJ / h; D h The steam extraction capacity for heating of gas units, unit: kg / h; h h It is the heating extraction enthalpy, unit: KJ / kg.

[0096] Gas unit heat consumption Q sr (kJ / h) is calculated using the formula:

[0097] Q sr =D0*(h0-h c )*d

[0098] Where: d is the steam consumption rate of the steam turbine, unit: kg / kwh; D0 is the total steam flow before the gas unit valve, unit: kg / h; h0 is the specific enthalpy of new steam, unit: KJ / kg; h c It is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg.

[0099] Gas unit heating cost allocation ratio β h and the power generation cost allocation ratio β q The formulas are used for calculation:

[0100] β q =Q sr / Q tp =Q sr / D ng LHV

[0101] β h =Q tp(h) / Q tp =Q tp(h) / D ng LHV

[0102] Where: β h is the heating cost allocation ratio of the gas unit; Q sr is the heat consumption of the gas unit, unit: KJ / h; Q tp is the total heat consumption of the cogeneration unit; Q tp(h) It is the heat consumption of the gas unit, unit: KJ / h.

[0103] Power generation cost allocation ratio of coal-fired units β r and real-time gas consumption rate for heating g gr (kg / kJ) are calculated using the formula:

[0104] β r =1-β q -β h

[0105] g gr =β h *D ng / Qgr

[0106] Where: β h It is the heating cost sharing ratio of the gas unit.

[0107] Real-time gas consumption rate of gas-fired unit power generation g q fd [kg / (kW·h)] and real-time gas consumption rate of coal-fired units rfd [kg / (kW·h)] are calculated using the formula:

[0108] g qfd =β q *D ng / E qf

[0109] g rfd =β r *D ng / E rf

[0110] Where: g qfd is the gas consumption rate of the gas-fired unit in real time, unit: kg / (kW·h); β q D is the cost allocation ratio of gas-fired units for power generation; ng is the natural gas flow rate, unit: kg / h; E qf is the real-time power generation of the steam turbine, unit: MW / h; g rfd is the gas consumption rate of the coal-fired unit in real time, unit: kg / (kW·h); β r E is the cost allocation ratio of coal-fired units; qf It is the real-time power generation of the gas turbine, unit: MW / h.

[0111] When evaluating the operating efficiency of coal-fired units, the real-time coal consumption rate is a key indicator. The coal consumption rate reflects the amount of coal consumed to produce a certain amount of energy during the power generation or heating process. The real-time coal consumption rates for power generation and heating are calculated separately in order to quantitatively analyze the energy utilization efficiency of coal-fired units. tp The calculation is as follows:

[0112]

[0113] Where: D is the main steam flow rate, unit: kg / h; h fw is the feed water enthalpy value, unit: kJ / kg; Q rh is the amount of reheat steam, unit: kg / h; h rh，0 、h rh，i They are the reheater outlet enthalpy and inlet enthalpy respectively, unit: kJ / kg.

[0114] External heat supply Qh (kJ / h) is calculated using the formula:

[0115] Q h =D h *(h h -h c )

[0116] Where: h h is the heating exhaust enthalpy, unit: KJ / kg; h c D is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg; h It is the steam extraction capacity for heating of gas-fired units, unit: kg / h.

[0117] Heat consumption Q for heating and electricity supply tp，h , Q tpe The formulas are used for calculation:

[0118] Q tp,e =Q tp -Q tp,h

[0119]

[0120] In the formula, Q tp,e is the heat consumption of the cogeneration unit; d is the steam consumption rate of the steam turbine, unit: kg / kwh.

[0121] The thermal efficiency of thermal power plants is calculated using the formula:

[0122]

[0123] Where: P e is the real-time power generation of the gas turbine unit, unit: MW / h; Q tp,e Heat consumption to supply electricity to the CHP group.

[0124] The coal consumption rate of thermal power units for power generation and the coal consumption rate of thermal power units for heating are calculated using the following formulas:

[0125]

[0126] Where: is the coal consumption rate of coal-fired units; Coal consumption rate for heating coal-fired units; η tp,e is the thermal efficiency of the combined heat and power unit.

[0127] In order to fully evaluate the economic feasibility of the multi-source heating system, this section further explores the costs of power generation and heating and the resulting profits. The cost calculation includes all relevant expenses such as fuel costs, system electricity and water consumption. The profit is the difference between revenue and cost, reflecting the profitability of the system. The cost C of power generation and heating is calculated using the formula:

[0128]

[0129] Where: P mi is the real-time power generation of the cogeneration unit i; Q mi Real-time heat supply for i cogeneration unit; K m is the coal price; K fd , K gr are the electricity price for power generation and the gas price for heating respectively; ri is the real-time power generation of coal-fired unit i; Q ri Real-time heat supply for i gas unit; f i is the real-time flow rate of natural gas for No. 1 gas boiler; d mi d ri d li i represents the real-time power consumption of coal-fired units, gas-fired units, and gas-fired boilers; s mi 、s ri 、s li Indicates the real-time water consumption of coal-fired units, gas-fired units and gas boilers.

[0130] The comprehensive economic benefit L generated is calculated using the formula:

[0131]

[0132] Where: k md k is the electricity selling price of the cogeneration unit; q is the price of steam for heating; k rd is the electricity selling price of the cogeneration unit; Q mi , Q qi and Q li is the real-time theoretical value of heat supply of gas boiler i.

[0133] In order to develop the economically optimal heating strategy for each unit, this study uses a particle swarm optimization algorithm (PSO) to maximize the comprehensive benefits of the heating system. In this process, the objective function L is first defined, which may represent the profit economic indicator, and aims to maximize it through the optimization strategy. During the optimization process, the following key constraints must be considered: (1) the maximum flow limit of the heating pipeline; (2) the maximum flow of the power center heating pipeline; (3) the minimum flow requirement of a single gas combined cycle unit during operation; (4) The heating capacity of each unit is directly related to its electrical load, which means that when formulating the heating strategy, the impact of electrical load on heating capacity must be considered.

[0134] By incorporating these constraints into the framework of the optimization algorithm, we ensure that the resulting strategy is not only cost-effective but also in line with the physical and technical limitations of actual operation. The output of the algorithm will guide us on how to allocate the heating flow of each unit to achieve the maximum heating benefit under given constraints.

[0135] This paper uses particle swarm optimization (PSO) to optimize the scheduling scheme, taking the maximum comprehensive economic efficiency as the evaluation criterion. The formula is used as the objective function:

[0136]

[0137] Where L(max) is the target value of economic profit. The larger its value is, the better the optimization result is, and vice versa. mix and P qix They respectively represent the real-time load correction values ​​of coal-fired unit i and gas-fired unit i after optimized scheduling.

[0138] In order to verify the feasibility of the scheduling scheme based on particle swarm optimization algorithm proposed in this paper, multiple groups of heat load distribution data of heating systems before optimization were selected, as shown in Table 3, and optimization analysis was performed on them to obtain the heat load distribution data after optimized scheduling of the multi-source heating collaborative control system, as shown in Table 4.

[0139] Table 3 Original heat load distribution data of multi-source heating system

[0140]

[0141]

[0142] Table 4 Heat load distribution data of multi-source heating coordinated control system

[0143]

[0144] In order to realize the comprehensive economic optimization scheduling of heating in multi-source heating collaborative control system, this paper adopts PSO algorithm to iterate 100 generations to generate 50 comprehensive economic optimal solutions, and obtains the evolution curve of comprehensive economic objective function value and iteration number, as shown in Figure 4 As shown in the figure, it can be seen that the algorithm has good convergence results, and the fitness finally converges to 15.9×104, which shows that the optimization result of the cooperative control system is good.

[0145] As shown in the data in Table 5, the heating supply and comprehensive economic performance before and after the optimization of the heating system are compared, and the following conclusions are drawn: Compared with the original heating system, the multi-source heating collaborative control system reduces the power of the gas boiler, and the gas boiler converts high-level energy into heat because it uses natural gas as fuel. Through the optimization analysis of the heating system, it is concluded that in the studied chemical park, the comprehensive economic performance of the entire heating system is improved by 1.21%.

[0146] Table 5 Comparison of multi-source heating system before and after optimization

[0147]

[0148] In summary: through the real-time data acquisition and economic evaluation module, the operating status and fuel consumption of the cogeneration unit can be monitored and obtained in real time, and the heating capacity curve under different operating conditions can be fitted, which is helpful to accurately predict and respond to load fluctuations and improve the stability and reliability of the heating system.

[0149] Energy optimization configuration problem: Through the optimization strategy generation and control execution module, the economic optimal heating strategy for each unit can be formulated based on the economic evaluation results and particle swarm optimization algorithm, realizing the optimal configuration and efficient utilization of energy, reducing operating costs and environmental pollution.

[0150] System flexibility issues: By considering the heating performance of the cogeneration unit under different load conditions and adjusting the operating status in real time, the flexibility of the heating system is enhanced, enabling the heating system to better adapt to demand fluctuations in the electricity market and respond to emergencies.

[0151] Working principle:

[0152] Data collection and monitoring: The real-time data collection module is responsible for monitoring and obtaining the operating status, fuel consumption and heat supply of the cogeneration unit. This includes the gas consumption and coal consumption of the gas-fired unit and the coal-fired unit under different operating conditions.

[0153] Economic evaluation: The economic evaluation module fits the heating capacity curve of the cogeneration unit under different operating conditions based on the data obtained by the real-time data acquisition module, calculates the fuel consumption rate under real-time conditions, including gas consumption rate and coal consumption rate, and calculates the economic profit value in combination with fuel cost, energy consumption, etc.

[0154] Optimization strategy generation: The optimization strategy generation module calculates the optimal value of the cogeneration unit based on the economic profit value and combined with the particle swarm optimization algorithm (PSO), which includes the heat flow and load distribution of each unit to maximize the comprehensive benefits of the heating system.

[0155] Control execution: The control execution module adjusts and optimizes the operating status of the cogeneration unit based on the optimization value. This includes adjusting the unit's load, fuel consumption, etc. to achieve the most economical heating strategy.

[0156] Consideration of constraints: During the optimization process, the maximum flow limit of the heating pipeline, the maximum flow of the power center heating pipeline, the minimum flow requirement of a single gas combined cycle unit during operation and other constraints are taken into account. At the same time, the heating capacity of each unit is directly related to its electrical load, ensuring that the physical and technical limitations of actual operation are met when formulating the heating strategy.

[0157] Algorithm convergence and results: The PSO algorithm is used for iterative calculation to generate the optimal solution for comprehensive economic performance. The algorithm has good convergence results, and the fitness eventually converges to a stable value, indicating that the optimization results of the collaborative control system are good.

[0158] System optimization effect: By comparing the heating amount and comprehensive economy before and after optimization, the optimization effect of the multi-source heating collaborative control system can be evaluated. The optimized system can reduce the power of the gas boiler and improve the comprehensive economic performance of the entire heating system.

[0159] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. An optimal economic algorithm for a multi-source heating system in an electricity market, comprising: Real-time data acquisition module, economic evaluation module, optimization strategy generation module and control execution module; characterized by: The real-time data acquisition module is used to monitor and obtain the operating status, fuel consumption and heat supply of the cogeneration unit in real time; The economic evaluation module fits the heating capacity curve of the cogeneration unit under different operating conditions based on the data acquired by the real-time data acquisition module, calculates the fuel consumption rate under the real-time state, and calculates the economic profit value based on the fuel consumption rate, combined with the fuel cost and the energy consumption of the cogeneration unit; The optimization strategy generation module calculates the optimization value of the cogeneration unit based on the economic profit value and in combination with a particle swarm optimization algorithm; The control execution module adjusts and optimizes the operating state of the cogeneration unit based on the optimization value.

2. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 1 is characterized by: The cogeneration unit includes a gas-fired unit and a coal-fired unit, and the fuel consumption includes the gas consumption of the gas-fired unit under different operating conditions and the coal consumption of the coal-fired unit under different operating conditions; the economic evaluation module calculates the gas consumption rate and coal consumption rate of the cogeneration unit under different operating conditions according to the gas consumption and coal consumption of the cogeneration unit under different operating conditions.

3. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 1 is characterized by: The calculation expression of the heating capacity curve of the cogeneration unit under different operating conditions is: In the formula: G1 is the heating capacity of the gas-fired unit under different operating conditions; Q1 is the load of the gas-fired unit under different operating conditions; a1, b1, c1, and d1 are all fitting coefficients. In this algorithm, a1=1.24, b1=0.0043, c1=0.00001, and d1=0.5503; G2 is the heating capacity of the coal-fired unit under different operating conditions; Q2 is the load of the coal-fired unit under different operating conditions; a2, b2, c2, and d2 are all fitting coefficients. In this algorithm, a2=0.66, b2=0.00288, c2=0.0000056, and d2=2.9917.

4. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 2 is characterized by: The calculation expressions of the gas consumption rate and coal consumption rate of the cogeneration unit under different operating conditions are as follows: g qfd =β q *D ng / AND qf g rfd =β r *D ng / AND rf Where: g qfd is the gas consumption rate of the gas-fired unit in real time, unit: kg / (kW·h); β q D is the cost allocation ratio of gas-fired units for power generation; ng is the natural gas flow rate, unit: kg / h; E qf is the real-time power generation of the steam turbine, unit: MW / h; g rfd is the gas consumption rate of the coal-fired unit in real time, unit: kg / (kW·h); β r E is the cost allocation ratio of coal-fired units; qf The real-time power generation of the gas turbine, unit: MW / h; is the coal consumption rate of coal-fired units; Coal consumption rate for heating coal-fired units; η tp,e is the thermal efficiency of the combined heat and power unit.

5. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 4 is characterized by: The power generation cost allocation ratio of the gas unit β q , the cost allocation ratio of coal-fired units β r And the thermal efficiency of the combined heat and power unit η tp,e The calculation expression is: β q =Q sr / Q tp =Q sr / D ng LHV β h =Q tp(h) / Q tp =Q tp(h) / D ng LHV β r ＝1-β q -β h Where: β h is the heating cost allocation ratio of the gas unit; Q sr is the heat consumption of the gas unit, unit: KJ / h; Q tp is the total heat consumption of the cogeneration unit; Q tp(h) is the heating consumption of the gas unit, unit: KJ / h; D ng LHV is Q tp The expression of LHV is the lower heating value of the fuel, unit: KJ / kg; η tp,e P is the thermal efficiency of the combined heat and power unit; e is the real-time power generation of the gas turbine unit, unit: MW / h; Q tp,e Heat consumption to supply electricity to the CHP group.

6. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 5 is characterized by: The heat consumption Q of the gas unit sr , heating consumption of gas unit Q tp(h) And the total heat consumption Q of the cogeneration unit tp The calculation expression is: Q sr =D0*(h0-h c )*d Q tp,e =Q tp -Q tp,h Where: D0 is the total steam flow before the gas unit valve, unit: kg / h; h0 is the specific enthalpy of new steam, unit: KJ / kg; h c Q is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg; t is the total heat consumption of the gas unit, unit: KJ / h; D h The steam extraction capacity for heating of gas units, unit: kg / h; h h is the heating extraction enthalpy, unit: KJ / kg; D is the main steam flow, unit: kg / h; h fw is the feed water enthalpy, unit: kJ / kg; Q rh is the amount of reheat steam, unit: kg / h; h rh，0 、h rh，i are the outlet enthalpy and inlet enthalpy of the reheater, respectively, unit: kJ / kg; Q tp,e Heat consumption for powering the CHP group; Q tp，h Q is the heat consumption of coal-fired units, in KJ / h; tp(h) The heat consumption of the gas unit for heating, unit: KJ / h; Q h Supply heat to the cogeneration unit; η b η p It is boiler efficiency and pipeline efficiency.

7. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 6 is characterized by: The total heat consumption Q of the gas unit t The heat supply Q of the cogeneration group h The calculation expression is: Q t =D gas *(h g -h ex ) Q h =D h *(h h -h c ) Where: h g is the flue gas enthalpy at the gas unit outlet, unit: KJ / kg; h ex is the flue gas enthalpy at the outlet of the waste heat boiler, unit: KJ / kg; D gas is the flue gas volume at the gas unit outlet, in kg / h; h h is the heating exhaust enthalpy, unit: KJ / kg; h c D is the specific enthalpy of steam at the outlet of the gas unit, in KJ / kg; h It is the steam extraction capacity for heating of gas-fired units, unit: kg / h.

8. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 1 is characterized by: The fuel cost calculation expression of the cogeneration unit in real time is: Where: P mi is the real-time power generation of the cogeneration unit i; Q mi Real-time heat supply for i cogeneration unit; K m is the coal price; K fd , K gr are the electricity price for power generation and the gas price for heating respectively; ri is the real-time power generation of coal-fired unit i; Q ri Real-time heat supply for i gas unit; f i is the real-time flow rate of natural gas for No. 1 gas boiler; d mi d ri d li i represents the real-time power consumption of coal-fired units, gas-fired units, and gas-fired boilers; s mi 、s ri 、s li Indicates the real-time water consumption of coal-fired units, gas-fired units and gas boilers.

9. The economic optimization algorithm of a multi-source heating system in an electricity market according to claim 1 is characterized by: The calculation expression of the economic profit value of the cogeneration unit is: Where: k md k is the electricity selling price of the cogeneration unit; q is the price of steam for heating; k rd is the electricity selling price of the cogeneration unit; Q mi , Q qi and Q li is the real-time theoretical value of heat supply of gas boiler i.

10. The economic optimization algorithm of a multi-source heating system in an electricity market according to claims 8 and 9 is characterized in that: The control execution module is set with the economic profit target value L(max) of the cogeneration unit; the control execution module takes the economic profit target value as the evaluation criterion, receives and optimizes the economic profit value, and the calculation expression of the economic profit target value L(max) is: In the formula, L(max) is the target value of economic profit. The larger its value is, the better the optimization result is, and vice versa. P mix and P qix They respectively represent the real-time load correction values ​​of coal-fired unit i and gas-fired unit i after optimized scheduling.

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