High-density distributed photovoltaic absorption-oriented regional energy network optimizing and scheduling method

Abstract

The invention discloses a high-density distributed photovoltaic absorption-oriented regional energy network optimizing and scheduling method. The method comprises the following steps: analyzing energy supply and energy storage apparatuses of a plurality of users in a zone energy network, and establishing energy supply mathematic models of the apparatuses; establishing a regional energy network optimizing and scheduling model which considers the high-density distributed photovoltaic absorption on the basis of centralized mutual connected energy exchange networks; in the regional energy network optimizing and scheduling model, analyzing the influences, caused by each user, on network energy loss, so as to enable the flow constraint of the network to be equivalent to power exchange constraint of the user and the energy network; enabling whole network energy loss, caused by the user, in a whole network loss calculation function in a target function to be equivalent to a product of the power exchange value of the user and the energy network and a network loss impact factor; and aiming at the characteristics of the model, solving the model by using an interior point method. Through scheduling the user energy supply apparatuses in the intelligent power grid zone and the operation manners and contribution of high-density distributed photovoltaics, the economic optimized operation of the energy network of the whole zone can be realized.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a method for optimal dispatching of regional energy networks oriented to high-density distributed photovoltaic consumption. Background technique [0002] Under the pressure of energy shortage, transformation of energy structure and energy saving and emission reduction, combined with active distribution network technology, by using flexible network topology to manage network energy flow, active control and control of distributed energy equipment in different regions Active management, and then the development of clean, reliable, interactive, and efficient energy utilization methods have become an important means to promote the economic transformation of the times and develop a low-carbon economy. The micro-energy network includes four energy forms of cold, heat, electricity and gas. It uses Internet of Things technology and information technology to integrate and dispatch al...

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Problems solved by technology

[0005] In terms of energy system interconnection modeling in China, some scholars have proposed an energy management optimization model for the distributed energy supply system of combined cooling and power supply, and selected the cooling and electrical load forecast data of typical months for optimal calculation, verifying the feasibility of the model However, the types of equipment in the cogeneration system are relatively limited, and only micro gas turbines are considered, and no corresponding models have been established for other auxiliary energy supply equipment; in terms of environmental protection factors, many scholars have taken environmental protection factors into consideration in the dispatching model, such as considering It is proposed to add the pollution gas emission penalty function to the objective function, and to establish a multi-objective opportunity programming model by comprehensively considering multiple factors such as operating costs and pollutant emissions, but it lacks the consideration of cold sources; some scholars have established a model that considers greenhouse gas and pollutant emissions. The microgrid economic model with the lowest operating cost of the microgrid as the objective function, and the particle swarm optimization algorithm is used to solve the above model, but the equipment composition of the CCHP system is still relatively single, which cannot adapt to the current development trend of energy interconnection systems

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