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An optimized operation method, system, equipment and medium of a combined electric heating system

A technology of combined systems and optimized operation, applied in the field of integrated energy system optimization, can solve problems such as affecting the accuracy of the solution, difficulty in solving, etc., to achieve the effects of strong spatial exploration, overcoming long computing time, and improving generation speed.

Active Publication Date: 2022-02-18
CHINA ELECTRIC POWER RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as the scale of the system continues to increase, and on the basis of considering the heat loss characteristics of the heating network, the multi-energy complementary optimization of the combined electric heating system presents high-dimensional nonlinear and non-convex characteristics. The traditional nonlinear solution method is difficult to solve, and the linearization process affects the solution. Accuracy, and existing traditional algorithms such as PSO (ParticleSwarm Optimization, particle swarm optimization algorithm), DDPG (Deep Deterministic Policy Gradien, deep deterministic policy gradient) are difficult to overcome the problem of information barriers between different stakeholders

Method used

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  • An optimized operation method, system, equipment and medium of a combined electric heating system
  • An optimized operation method, system, equipment and medium of a combined electric heating system
  • An optimized operation method, system, equipment and medium of a combined electric heating system

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Embodiment 1

[0127] In the technical solution provided by the embodiment of the present invention, an optimal scheduling model of the combined electric heating system based on the multi-agent deep deterministic strategy gradient is constructed to realize the multi-energy coordinated optimal scheduling of the combined electric heating system. Compared with the traditional model, it effectively solves the sequence decision-making problem in the continuous control process, avoids the disadvantages caused by the use of discrete action spaces, and only needs to know the local state information of each agent to complete their respective strategy calculations, and solves the problem of different agents. data sharing issues. In addition, combined electric heating system (such as described in the following literature, [1] Wang Weiliang, Wang Dan, Jia Hongjie, etc. A review of steady-state analysis of typical regional comprehensive energy systems under the background of energy Internet [J]. Chinese J...

Embodiment 2

[0134] Based on Example 1 above see image 3 Optionally, in the embodiment of the present invention, the combined electric heating system includes: a conventional generator set, a wind turbine, a combined heat and power device, etc.; wherein, G1 and G2 represent conventional generator sets, which are responsible for supplying electric loads in the system; W1 represents a wind turbine , the influence of its maximum output, wind speed, etc. is random, and its maximum output needs to be obtained according to the forecast results; CHP1, CHP2 represent cogeneration devices, which can supply the internal heat load of the system while supplying the electrical load in the system; load1, load2, load3 represents the electric load in the system; Hload1, Hload2, and Hload3 represent the heat load in the system.

[0135] For explanatory purposes, since the combined electric heating system is already an existing technology (refer to the references given above), a brief description is given ...

Embodiment 3

[0137] Based on Example 1 above see Figure 4 and Figure 5 , optional in the embodiment of the present invention, the multi-agent deep reinforcement learning model such as Figure 4 As shown, including: basic elements such as agent, environment, action, state and reward function.

[0138] The internal structure of the agent is as Figure 5 As shown, each agent is composed of a strategy (Actor) network and a value function (Critic) network. The agent perceives the state (s) from the environment, and the state set is input into the strategy network, and the strategy of the agent is obtained through the neural network calculation. , outputs all actions (a) of the agent in a given state. Specifically, the present invention divides the power system and the thermal system into two agents in the above model respectively.

[0139] Environment: Contains basic mathematical models of energy flow in electrical and thermal systems.

[0140] Exemplarily, regarding the power system mod...

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Abstract

The invention discloses an optimized operation method, system, equipment and medium of a combined electric heating system. The method includes the following steps: acquiring state parameters of the combined electric heating system to be operated optimally; wherein, the state parameters include: electric load, Wind power maximum output, thermal load and ambient temperature; input the state parameters into the pre-trained multi-agent deep reinforcement learning model, and output the action amount through the multi-agent deep reinforcement learning model; wherein, the action amount includes: The generating power of the conventional unit, the generating power of the combined heat and power device, the wind power generating power, and the heat generating power of the combined heat and power device; based on the amount of action, the optimized operation of the combined electricity and heat system is realized. The method or system provided by the present invention can realize multi-energy coordination and optimal scheduling of combined electric and heat systems.

Description

technical field [0001] The invention belongs to the technical field of comprehensive energy system optimization, and relates to a combined electric heating system, in particular to an optimized operation method, system, equipment and medium of a combined electric heating system. Background technique [0002] In the context of energy Internet, improving energy utilization efficiency, promoting the consumption of renewable energy, realizing sustainable energy development and reducing environmental pollution are the development goals of the current energy system. The combined electric heating system is an important physical carrier of the Energy Internet, the key to the application of concepts such as multi-energy complementarity and energy cascade utilization, and an important development direction for the current energy structure adjustment. The research on the integrated energy system coupled with the power system and the heating system is of great significance for breaking ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/08G06N3/048G06N3/045Y02E40/70Y04S10/50
Inventor 蒲天骄董雷李烨王新迎王继业
Owner CHINA ELECTRIC POWER RES INST
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