Comprehensive energy low-carbon scheduling circuit considering carbon capture and demand response

Abstract

The utility model provides a comprehensive energy low-carbon scheduling circuit considering carbon capture and demand response. The comprehensive energy low-carbon scheduling circuit comprises an input circuit, a decision circuit and a prediction circuit. Wherein the decision circuit is connected with the input circuit and the prediction circuit respectively; the input circuit is connected with the scheduling system and the monitoring system; the decision-making circuit is further connected with a plant area system, and the plant area system is at least provided with power-to-gas equipment, a carbon capture power plant and a new energy power plant. A multi-stage operational amplifier is arranged between the signal input end and the signal output end of the input circuit; the decision circuit is a double-end input and single-end output differential signal circuit; the prediction circuit is a neural network analog circuit. According to the utility model, the structure is simple, the cost is low, through circuit combination, simple instructions can be analyzed, and an execution strategy can be automatically optimized through a prediction circuit, so that efficient utilization of clean energy and effective control of carbon emission are realized, and the economical efficiency and the low-carbon property of the system are improved.

Description

TECHNICAL FIELD

[0001] The utility model relates to new energy technology field, concretely relates to a kind of comprehensive energy low carbon dispatching circuit considering carbon capture and demand response. BACKGROUND

[0002] To respond to carbon reduction demand, under the integration of cold, heat, electricity, gas and other multiple energy, comprehensive energy system gradually becomes the key technology to improve energy utilization efficiency and reduce carbon emissions.

[0003] However, due to the current proportion of thermal power in energy structure is still large, there is a contradiction between its high carbon emission characteristics and low carbon development demand. Although carbon capture technology can reduce carbon emissions of thermal power, traditional carbon capture is low in efficiency and high in energy consumption under high load, which is difficult to meet the demand of large-scale emission reduction. At the same time, the increase of installed capacity of clean energy such as wind power and photovoltaic makes the intermittency and volatility of power supply increasingly prominent, and the existing energy storage and other peak shaving means are difficult to support the dynamic demand of the system in capacity and cost. Therefore, it is necessary to control and adjust the combined power generation system / power station based on demand response.

[0004] Although demand response technology can cut peak and fill valley on the load side, it is limited to single power system and difficult to realize collaborative optimization in multiple energy scenarios, resulting in low consumption rate of clean energy. The application of comprehensive dispatching and control system in multiple energy scenarios generally needs highly customized design and construction of complex control system, which not only has high cost but also requires high maintenance, and the system maintenance cost is high.

[0005] In view of the above, it is necessary to develop a control circuit of comprehensive energy low carbon dispatching system, which can effectively simplify the system structure and supporting products under the existing control logic, on the one hand, can reduce cost and failure rate, on the other hand, can realize efficient utilization of clean energy and effective control of carbon emissions through flexible carbon capture, demand response strategy of multiple energy cooperation and step carbon trading mechanism, so as to improve the economy and low carbon of the system. UTILITY MODEL CONTENT

[0006] The utility model provides a kind of control circuit of comprehensive energy low carbon dispatching system with simple structure and low cost for the problems existing in prior art.

[0007] In order to achieve the above purpose, the technical scheme adopted by the utility model is as follows:

[0008] The application discloses a kind of integrated energy low carbon scheduling circuits considering carbon capture and demand response, mainly including input circuit, decision circuit and prediction circuit;The decision circuit is connected with the input circuit and the prediction circuit respectively;The input circuit is connected with scheduling system and monitoring system;The decision circuit is also connected with plant system, and the plant system is at least provided with electric gas conversion equipment, carbon capture power plant and new energy power generation field;

[0009] A plurality of operational amplifiers are arranged between the signal input end and the signal output end of the input circuit;

[0010] The decision circuit is a differential signal circuit with double-ended input and single-ended output;

[0011] The prediction circuit is a neural network simulation circuit.

[0012] Optionally, the input circuit includes two operational amplifiers, which are a first operational amplifier and a second operational amplifier;

[0013] The signal input end is connected with the non-inverting input end of the first operational amplifier, and the inverting input end and the output end of the first operational amplifier are connected with the non-inverting input end of the second operational amplifier;

[0014] The inverting input end and the output end of the second operational amplifier are connected with the signal output end.

[0015] Optionally, the signal input end of the input circuit is also provided with an RC filter circuit.

[0016] Optionally, the non-inverting input end of the second operational amplifier is provided with a pull-down resistor circuit, and the output end is provided with a bypass capacitor circuit.

[0017] Optionally, the signal output end of the input circuit is provided with an output signal limiting circuit.

[0018] Optionally, a filter circuit is arranged between the double inputs of the decision circuit.

[0019] Optionally, a thin film capacitor is connected across the double inputs of the decision circuit.

[0020] Optionally, the filter circuit is a π-type capacitor filter circuit, and the capacity of the capacitor ranges from 1 μF to 1.5 μF.

[0021] The capacity of the thin film capacitor ranges from 1 μF to 5 μF.

[0022] Optionally, the neural network simulation circuit includes an input layer circuit, a hidden layer circuit and an output layer circuit.

[0023] The input layer circuit is an attenuation or voltage dividing resistor network.

[0024] The hidden layer circuit is a neuron activation function module and an operational amplifier combined circuit.

[0025] The output layer circuit is a weighted calculation circuit or a node circuit.

[0026] Optionally, the neuron activation function module is a hyperbolic tangent function negative operation function module.

[0027] Compared with the prior art, the utility model has the following beneficial effects:

[0028] The utility model discloses simple structure, low in cost, through the circuit combination, not only can resolve simple instruction, still can realize the automation optimization execution strategy through the prediction circuit, thereby realizes the efficient use of clean energy and the effective control of carbon emission, and further improves the economy and low carbon of system. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical scheme in the embodiments of the utility model or prior art, the following will briefly introduce the drawing needed to be used in the embodiments, and obviously, the drawing in the following description is only some embodiments of the utility model, and for those skilled in the art, other drawings can also be obtained according to these drawings without paying the creative labor.

[0030] Figure 1 It is the system diagram of the utility model;

[0031] Figure 2 It is the input circuit diagram in the specific embodiment of the utility model;

[0032] Figure 3 It is the decision circuit diagram in the specific embodiment of the utility model;

[0033] Figure 4 It is the prediction circuit diagram in the specific embodiment of the utility model. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantage of the embodiments of the utility model more clear, the following will combine the drawing in the embodiments of the utility model, and the technical scheme in the embodiments of the utility model is clearly and completely described, obviously, the described embodiment is a part of the embodiment of the utility model, rather than all the embodiments of the utility model.Based on the embodiment in the utility model, all other embodiments obtained by those skilled in the art without paying the creative labor are within the protection scope of the utility model.

[0035] It should be noted that: similar signs and letters show similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the following drawings.

[0036] In the description of this utility model, "multiple" means two or more, unless otherwise explicitly specified.

[0037] In this utility model, unless otherwise explicitly specified and limited, the terms "installation," "connection," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this utility model according to the specific circumstances.

[0038] It is worth noting that, unless otherwise specified, the methods used in this utility model are all conventional methods; and the raw materials and equipment used are all conventional commercially available products, and their sources are not specifically limited.

[0039] like Figure 1 As shown, this embodiment provides a comprehensive low-carbon energy dispatching circuit that considers carbon capture and demand response. It mainly includes an input circuit, a decision circuit, and a prediction circuit. The decision circuit is connected to both the input circuit and the prediction circuit. The input circuit is connected to both the dispatching system and the monitoring system. The decision circuit is also connected to the plant system, which includes at least an electricity-to-gas conversion device, a carbon capture power plant, and a new energy power plant. In this embodiment, the new energy power plant includes wind turbines and photovoltaic power plants.

[0040] Among them, such as Figure 2 As shown, a multi-stage operational amplifier is provided between the signal input terminal and the signal output terminal of the input circuit in this embodiment; optionally, the input circuit includes two operational amplifiers, namely a first operational amplifier F1 and a second operational amplifier F2.

[0041] The signal input terminal connects to the dispatching system and the monitoring system, and is used to receive initial dispatching instructions and various indicators obtained from monitoring. The signal input terminal is connected to the non-inverting input (+) of the first operational amplifier F1, and an RC filter circuit, including a 0.5kΩ resistor R, is connected on the signal input line. s And a 0.1μF capacitor C1.

[0042] The inverting input (-) and output of the first operational amplifier F1 are connected, and then connected to the non-inverting input (+) of the second operational amplifier F2 through a 12kΩ resistor R2. The non-inverting input (+) of the second operational amplifier F2 is connected to ground via a pull-down resistor circuit, specifically through a 1.5kΩ resistor R3. The inverting input (-) of the second operational amplifier F2 is connected to ground through a 1.5kΩ resistor R... fThe non-inverting input terminal (+) of the first operational amplifier F1 is connected to the input terminal of the decision circuit through a 12kΩ resistor R2, and the inverting input terminal (-) of the first operational amplifier F1 is connected to the input terminal of the prediction circuit through a 12kΩ resistor R3.

[0043] The output terminal of the second operational amplifier F2 is connected to the signal output terminal; optionally, the line of the output terminal of the second operational amplifier F2 is provided with a bypass capacitor circuit and an output signal limiting circuit. The bypass capacitor circuit is a 0.1μF capacitor C2 connected to the ground. The output signal limiting circuit is a 3.0V zener diode D1 connected to the ground.

[0044] As shown in Figure 3 , the decision circuit is a differential signal circuit with double input terminals and single output terminal; optionally, a thin film capacitor C4 with a capacity ranging from 1μF to 5μF is connected across the double input terminals VBP and VBN of the decision circuit, and the capacity of the thin film capacitor C4 is preferably 4.7μF in this embodiment.

[0045] A filter circuit is provided between the double input terminals VBP and VBN of the decision circuit. The filter circuit is a π-type capacitor filter circuit, which includes capacitors C1, C2 and C3, and the middle capacitor C3 is connected to the ground, and the capacity of each capacitor ranges from 1μF to 1.5μF; the capacity of each capacitor is preferably 1μF in this embodiment.

[0046] A 20kΩ resistor R4 and a resistor R5 are respectively connected in series to the double input terminal line of the decision circuit.

[0047] As shown in Figure 4 , the prediction circuit is a neural network simulation circuit, which includes an input layer circuit, a hidden layer circuit and an output layer circuit.

[0048] Among them, V a , V b , V c are input signals, which are transmitted to the next layer through a voltage dividing resistor network R6-R13 in the input layer circuit. Further, V a , V b are taken as negative values by two inverting amplifiers U2 and U4 and their circuits to join the next layer circuit.

[0049] The hidden layer circuit is a combination circuit of neuron activation function module and operational amplifier; optionally, the neuron activation function module is a function module of hyperbolic tangent function taking negative operation (-tanh) Tg1-Tg3, specifically, the function module of hyperbolic tangent function taking negative operation can be set by circuit integration of hyperbolic tangent function, which is not described here. Three operational amplifiers U1, U3 and U5 are respectively connected to a first-order RC circuit and a function module of hyperbolic tangent function taking negative operation at the reverse input terminal (-) and the output terminal, and are connected to the output layer.

[0050] The output layer circuit of this embodiment is a node circuit, which is used to output signals V1, V2 and V3 after neural network processing.

[0051] Working process:

[0052] The first step: the system receives the instructions of power grid dispatching, and transmits the instructions to the system input circuit module, and combines in the load adjustment process, the system monitors various index signals of the thermal power plant in real time; at the same time, filtering operation is carried out, and the actual net power of the power plant is obtained.

[0053] The second step: the carbon capture system composed of the absorption tower, the regeneration tower and other carbon capture equipment is used for CO2 capture of the carbon-containing flue gas of the thermal power plant, and the carbon capture power plant is formed. In the flue gas diversion control process, the decision circuit of the utility model receives the signal of the previous stage. And through the differential circuit, the actual net power of the thermal power plant at this moment is compared with the theoretical optimal net power, if the actual net power is lower than the optimal net power, the flue gas diversion form is taken to dynamically adjust the proportion of the carbon-containing flue gas of the power plant flowing into the carbon capture system; if the actual net power is higher than the optimal net power, the whole carbon-containing flue gas of the power plant is discharged into the carbon capture system.

[0054] The third step: under the combined operation mode, the CO2 captured by the carbon capture power plant can be supplied to the electricity-to-gas (P2G) equipment to generate natural gas under the energy supply of wind power and light power. The prediction circuit receives the input signal composed of the real-time data collected by the sensor and the historical power consumption data (database storage data), and stores these data in the nonvolatile memory, and the data is cleaned by the pretreatment module such as filter to remove noise and smooth data, so as to extract effective features. The core prediction calculation module adopts a neural network hardware accelerator for prediction. Finally, the prediction result, that is, the optimal net power, is transmitted to the decision circuit of the previous step through the output port, which is used to judge whether there is large-scale wind and light abandonment in the future, so as to determine whether the P2G equipment is started to consume the abandoned wind and light to generate natural gas.

[0055] Finally, it should be explained that the above content is only used to explain the technical scheme of the utility model, and is not limited to the protection scope of the utility model. The simple modification or equivalent replacement of the technical scheme of the utility model by the ordinary skilled in the art does not deviate from the essence and scope of the technical scheme of the utility model.

Claims

1. A low-carbon scheduling circuit for integrated energy considering carbon capture and demand response, characterized in that: The input circuit, the decision circuit and the prediction circuit are included; the decision circuit is connected with the input circuit and the prediction circuit respectively; the input circuit is connected with the scheduling system and the monitoring system; the decision circuit is also connected with the plant system, which is provided with at least the electric-gas conversion equipment, the carbon capture power plant and the new energy power generation field; A plurality of operational amplifiers are arranged between the signal input end and the signal output end of the input circuit; The decision circuit is a differential signal circuit with double input ends and single output end; The prediction circuit is a neural network simulation circuit. 2.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response according to claim 1, wherein: The input circuit includes two operational amplifiers, which are a first operational amplifier and a second operational amplifier; The signal input end is connected with the non-inverting input end of the first operational amplifier; the inverting input end and the output end of the first operational amplifier are connected with the non-inverting input end of the second operational amplifier; The inverting input end and the output end of the second operational amplifier are connected with the signal output end. 3.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response according to claim 1 or 2, characterized in that: The signal input end of the input circuit is also provided with an RC filter circuit. 4.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response of claim 2, wherein: The non-inverting input end of the second operational amplifier is provided with a pull-down resistor circuit, and the output end is provided with a bypass capacitor circuit. 5.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response according to claim 2 or 4, characterized in that: The signal output end of the input circuit is provided with an output signal limiting circuit. 6.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response of claim 1, wherein: A filter circuit is arranged between the double input ends of the decision circuit. 7.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response according to claim 6, wherein: A thin film capacitor is connected across the double input ends of the decision circuit. 8.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response according to claim 7, wherein: The filter circuit is a π-type capacitor filter circuit, and the capacity of the capacitor ranges from 1 μF to 1.5 μF; The capacity of the thin film capacitor ranges from 1 μF to 5 μF. 9.The integrated energy low-carbon dispatching circuit considering carbon capture and demand response of claim 1, wherein: The neural network simulation circuit includes an input layer circuit, a hidden layer circuit and an output layer circuit; The input layer circuit is an attenuation or voltage division resistor network; The hidden layer circuit is a combination circuit of neuron activation function module and operational amplifier; The output layer circuit is a weighted calculation circuit or a node circuit.

10. The integrated energy low-carbon dispatch circuit considering carbon capture and demand response according to claim 9, characterized in that: The neuron activation function module is a functional module of hyperbolic tangent function taking negative operation.

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