Electricity-gas comprehensive energy system collaborative optimization method

Abstract

The invention discloses an electricity-gas comprehensive energy system collaborative optimization method, which comprises the steps of establishing an electricity-gas comprehensive energy system collaborative optimization operation model, and randomly generating particle positions and movement speeds; calculating a decision variable of the electricity-gas comprehensive energy system; performing energy flow calculation according to the decision variable of the electricity-gas comprehensive energy system; calculating the fitness of the particles, and updating the optimal positions and speeds ofthe particles; and carrying out loop iteration is carried out till the set maximum number of iterations is reached. The complex network constraint optimization problem is solved by combination of energy flow calculation of the electricity-gas comprehensive energy system and the particle swarm algorithm, and the feasibility and economy of the scheduling and planning method of the electricity-gas comprehensive energy system are enhanced.

Description

technical field [0001] The invention relates to the technical field of energy system optimization operation methods, in particular to a collaborative optimization method for an electric-gas comprehensive energy system. Background technique [0002] With the continuous deepening of the coupling between the power system and the natural gas system, the optimal operation of the electricity-gas integrated energy system has become a current research hotspot. However, the power and natural gas system model is relatively complex, and even the steady-state model that does not consider the dynamic process has strong non-convex nonlinearity, which brings great challenges to the solution of the optimization algorithm. [0003] Previous studies started from two perspectives. One is to simplify the electrical-pneumatic coupling system and transform it into a model form that can be solved by a commercial solver. This method has the disadvantage of inaccurate models, and in the gas network...

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Problems solved by technology

However, the existing research based on this method has not considered the complex network constraints in the coupled system, which may lead to the problem that the scheduling strategy is not feasible.

Although the intelligent algorithm can realize the optimal solution of complex mathematical models, it is necessary to transform the optimization problem with constraints into an unconstrained optimization problem, and it is difficult to obtain the coupling after the decision variables of the electrical-pneumatic coupling system (unit output, gas production rate) are known. The energy flow of the system makes it impossible to write the coupled system network constraints into the objective function as a penalty term

For example, the particle swarm intelligent algorithm expresses unit output and gas well production as particle positions, and adds the upper and lower limits of unit output, slope climbing, and gas well production constraints as penalty functions to the particle evaluation function, or optimizes the optimal particle according to the above constraints. Corrections are made, but network topology constraints in integrated energy systems are not considered

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