为实现高比例可再生能源的“非电利用”,对集中供热系统引入可再生能源站（由电锅炉和电动空气源热泵构成）,提高系统对波动性可再生能源的利用率及经济性。在集中供热系统中针对新增可再生能源站配置数量、接入位置及装机容量分配方案建立同步优化模型,该模型分为内、外两个优化模块,内部优化一级网循环泵的总功耗成本以及供热系统对波动性可再生能源的利用率,外部优化全生命周期的经济收益净现值,在项目全生命周期内以内、外两个模块的综合最优为目标,利用遗传算法实现高效全局优化。通过案例分析表明,在供热系统中新增能源站之后,通过该同步优化模型,整个集中供热系统的水力工况得到优化,管网全部热力站用户的平均资用压差减小,消耗在输配管网和热力站调节阀上的功耗显著降低,并带来可观的节能经济收益。
Abstract
In order to achieve a high proportion of non-electric utilization of renewable energy, we introduced renewable energy stations （consisting of electric boilers and electric air source heat pumps） into the district heating system to enhance the utilization rate and economic efficiency of fluctuating renewable energy. To address the integration of the newly added renewable energy stations in the district heating system, we established a synchronized optimization model for configuring the quantity, connection points, and installed capacity. The model comprises two optimization modules： the internal module optimizes the total power consumption cost of the primary network circulation pump and the utilization of fluctuating renewable energy in the heating system, while the external module optimizes the net present value of economic benefits throughout the project's lifecycle. The comprehensive optimization of both internal and external modules is the primary objective, and genetic algorithms are employed to achieve efficient global optimization. The case study results demonstrate that after adding energy stations to the heating system, the synchronized optimization model optimizes the hydraulic conditions of the entire district heating system, reduces the average pressure difference among all heating station users in the network, significantly decreases energy consumption in the distribution network and heating station control valves, and results in substantial energy-saving economic benefits.
关键词
可再生能源 /
成本效益分析 /
优化 /
区域供热 /
容量分配 /
全生命周期效益
Key words
renewable energy /
cost benefit analysis /
optimization /
district heating /
capacity allocation /
whole life cycle benefit
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基金
河北省高等学校科学技术研究项目（QN2021042）; 河北省自然科学基金（E2024202065）; 国家自然科学基金（52208104）
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