Distributed comprehensive energy demand response collaborative optimization method for intelligent building group

Abstract

The invention discloses a distributed comprehensive energy demand response collaborative optimization method for an intelligent building group. The distributed comprehensive energy demand response collaborative optimization method comprises the following steps: establishing a plurality of single-building multi-energy flow energy systems based on an energy hub concept in an energy internet; interconnecting a plurality of single-building multi-energy-flow energy systems through a power transmission line, a heating pipeline and a natural gas pipeline to form a multi-building multi-energy-flow energy interconnection system; establishing a mathematical model of the multi-building multi-energy flow energy interconnection system, wherein the mathematical model comprises a building energy consumption objective function and a building energy consumption constraint condition; and carrying out optimization solution on the mathematical model by utilizing an alternating direction multiplier algorithm to obtain an intelligent building optimal energy utilization strategy. The distributed comprehensive energy demand response collaborative optimization method can effectively reduce the electrical load of a power utilization peak, optimize the energy consumption structure of a user, delay the plan of power supply and distribution equipment upgrading and capacity expansion, reduce the economic pressure of an energy supplier due to relatively high investment cost, promote information privacy protection and local decision autonomy, and greatly improve the overall scheduling decision response speed.

Description

technical field [0001] The invention relates to the technical field of energy management, in particular to a distributed comprehensive energy demand response collaborative optimization method for intelligent building groups. Background technique [0002] The large number of electric vehicles and energy storage systems connected to the smart building system will generate new load peaks at the time of low electricity price, and the power will exceed the capacity transmission limit of the system. The existing methods mainly solve the above problems by building new power supply and distribution equipment and load shedding. However, new power supply and distribution equipment will put greater economic pressure on power suppliers due to the investment cost of power equipment, and load shedding will affect the comfort of end users for end users. With the increasing application range of distributed electricity-gas-heat conversion equipment, the object of demand response has expande...

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Problems solved by technology

However, the new power supply and distribution equipment will bring greater economic pressure to power suppliers due to the investment cost of power equipment, and load shedding will affect the comfort of end users in terms of power consumption for end users.

With the increasing application range of distributed electricity-gas-heat conversion equipment, the object of demand response has expanded from a single electric energy flow to a multi-energy flow system including electricity, heat, and natural gas. The strong coupling of electricity, heat, and gas in a multi-energy flow system The relationship and the requirement of collaborative optimization of multiple energy sources in the solution process not only increases the difficulty of problem solving, but also makes the amount of transmitted data of traditional distributed algorithms increase sharply, which is not conducive to the application of large-scale comprehensive demand response optimization process

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