Graph-based algorithmic design and decision-making framework for district heating and cooling plant positioning and network planning

Highlights

• Algorithmic design and decision-making framework for district energy system.

• District heating and cooling site location and network layout optimization.

• Multi-objective evaluation and data-driven decision support.

• Graph-based approach for district heating and cooling system design.

Abstract

Although buildings are the single largest users of energy in cities, many individual building HVAC (heating, ventilation, and air conditioning) systems are energy inefficient. Sustainable district heating and cooling systems have been developed to address this inefficiency, however, the implementation of district heating and cooling systems is a complex, capital intensive multivariate problem. One critical engineering problem, the location of energy production plant and pipe network topology, has a major influence on the system performance as well as the life cycle cost, and therefore should be comprehensively studied and chosen. This paper proposes a novel algorithmic design and decision-making framework that uses multiple simultaneous criteria to generate and evaluate design alternatives of site selection and pipe network layout. The framework consists of three components: site location and topology generation, network sizing and evaluation, and multi-objective decision-making assistance. The proposed framework has been validated in a real-world district cooling greenfield project in China's metropolitan area. Results were compared against the engineers’ best practices. It has shown that the proposed framework could find equally good or better designs in engineering and financial performance with CAPEX reduction of up to 15%.

Introduction

As the world rapidly urbanizes and strives towards carbon neutrality, district energy systems have been adopted in urban areas because of their multiple advantages. A district heating and cooling (DHC) system centralizes heating or cooling energy production and distributes the generated hot water or chilled water to all end-users, including residential, commercial, industrial, or other users, through an underground pipe network. Compared to traditional individual HVAC (heating, ventilation, and air conditioning) systems, district energy systems are capable of higher energy efficiency, lower greenhouse gas emissions, improved air quality, and a better economy of scale [1], [2], [3], [4].

Implementing district heating and cooling systems in urban areas is of significant spatiotemporal complexity. Temporally, lifecycle of district heating and cooling system spans many different stages: from master planning, design, to construction and operation, each of which has impact on the overall lifecycle performance. During master planning of greenfield development, many different domains are recursively involved to determine the different infrastructure components: road planning, land use definition, thermal energy planning, power generation, gas supply, etc [5], [6]. Planning at this early stage is associated with enormous uncertainty but at the same time embodies maximum flexibility and potential for improvement of the design [7]. Spatially, district heating and cooling systems relate to large urban areas and infrastructure, consisting of centralized energy supply system design, distribution system design, and end-user buildings, where many design decisions need to be made.

Among all, positioning of the energy production plant and routing of the network topology, which by nature are the most impactful, should be prioritized and holistically considered, since they significantly impact the system performance and changes are difficult to be made once constructed. On the one hand, the initial network cost including auxiliary pumping system and related civil work typically accounts for nearly 40% − 75% of the district heating and cooling system’s overall cost [8], [9]. On the other hand, a successful plant positioning could balance the load positions on the water network and reduce the pumping cost. Unlike other relatively independent design decisions like energy supply equipment selection, site location and network planning are deeply involved in the early master planning of the whole area as shown in Fig. 1. Consequently, considerations other than engineering design are needed (political, societal, military, etc.). Thus, an optimal planning scheme associated with lower cost and better performance benefits most stakeholders - including government, investors, operators, end-users, etc., and therefore should be carefully considered in order to improve subsequent design and operation.

In the domain of district thermal energy network planning optimization, there are three main categories: 1) supply and demand configuration optimization for network connection; 2) topology optimization (including site selection), and 3) diameter optimization. While many studies have been done on supply and demand optimization and diameter optimization, relatively few studies have focused on the topology optimization because of the large scale and high variability and complexity of such convoluted planning problems. Despite the scarcity of topology optimization studies, it is critically important as it has tremendous impact on other urban systems and should be prioritized before other potential improvements.

In current manual engineering practice, very few network topology design and energy production plant positioning alternatives are studied during the conceptual design phase of the city’s energy system planning, since they rely on engineers’ experience and judgment that are rarely quantified explicitly, leaving huge potentials remaining unexplored. Besides, in the initial project stage, actual conditions and constraints may change at any time, making it even more difficult for engineers to quickly and comprehensively consider different design alternatives.

To solve these issues, extensive studies [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21] have been conducted to improve the design of the district heating and cooling systems by using graph-based modeling, as the network design problem by nature is to find paths that form a subgraph as the distribution network in an even larger graph, in order to satisfy certain objectives. However, most of this existing research either simplifies the problem domain without considering the actual geographical situation or does not consider the positioning of the energy production plant. Therefore, there is a need for a computer-aided decision-making framework that leverages computational power to enable engineers to fully explore many different designs and their engineering and financial performance in order to make informed decisions.

This paper aims to develop a novel algorithmic design and decision-making framework that uses multiple simultaneous criteria to generate and evaluate design alternatives of site selection and pipe network layout. The rest of the paper is organized as follows: in Section 2 the existing literature is reviewed and discussed. Section 3 introduces the methodology of the proposed method. In particular, Section 3.1 introduces an extensible design generation framework and algorithm proposed by the authors. Section 3.2 introduces the implemented network sizing and evaluation criteria, some of which are the common principles shared by local design and engineering firms, some of which are proposed by the authors to complement in order to provide a more comprehensive analysis. Section 3.3 introduces ways to present the evaluation results for decision support with the use of parallel coordinates graph and computation of pareto front which are usually seen in multi-objective optimization studies. Section 4 presents the use of the proposed method in a real-world project as a case study. Section 5 shows a validation example against the engineers’ designs and a widely used genetic algorithm. Section 6 discusses the limitations and the future research. Finally, the key findings and contributions are summarized in Section 7.