Management of building energy consumption and energy supply network on campus scale

This thesis develops a model for energy performance assessment to support energy efficient design at district scale focusing on the multiple relationships between energy consumers and producers in the district. The model uses (1) a building energy model to quantify the energy performance of buildings as energy consumers on an hourly basis, and (2) network to analyze energy flows and quantify the overall performance of a wide variety of energy supply systems shared by buildings (energy consumers). The network energy model represents energy consumers and energy producers on the community level, allowing alternative ways to connect them in an overall energy supply topology. The essence of the model is a directed graph, consisting of nodes and connectors (arcs). A node represents an energy consumer or producer and arcs represent ways in which they are connected. Arcs come in different types, each type representing a particular way in which a supplier and consumer can be connected. Building nodes represent energy consumers at the highest level. At a lower level, a building node contains sub-nodes that represent the individual consumer systems (heating, cooling, lighting, fans, pumps, domestic hot water, and other services) in a building. Producer nodes represent various electrical power and thermal energy supply systems, including power generation from fossil fuel power plants (this is typically an external node), renewable source systems and thermal energy distribution from district heating and cooling systems, in conjunction with combined heat and power plants. After a graph is constructed and all properties of the system nodes are provided, the calculation runs in the background and shows energy consumption and generation at the network level as well as the node level in a given climate. Each arc that crosses a node represents a quantity of purchased or delivered energy flowing to or from the node. The NEP model allows campus wide energy performance assessment testing different supply topologies, i. e. which consumer nodes connect to which local suppliers and which connect to global suppliers (i. e. utility providers such as the electricity grid or the natural gas grid). The prototype implementation shows how a portfolio or campus manager defines a model of the consumer and supply nodes on a campus and manipulates the connections between them through a graphical interface. Every change in the graph automatically triggers an update of the energy generation and consumption pattern, and results in a campus-wide energy performance update. It helps macro decisions on the generation side (such as decisions about adding campus wide systems) and the consumption side (such as planning of new building designs and retrofit measures). This model provides a lightweight tool that supports rapid decision making for energy efficient system design on a portfolio scale in the building sector. There is no deep simulation required as the goal is to manage macro design decisions, not micro operational decisions. The premise of this approach is that an energy performance assessment of each node, based on normative calculation methods, is accurate enough to support macro, system-level decision making. The model is scalable to larger portfolios and systems, and is flexible enough to explore different topologies by adding or taking away nodes. The main distinguishing feature is the way that nodes and their connections can be managed in the graphical interface while the underlying representation maintains the consistency to perform fresh calculations at any time. Compared to approaches used in the smart grid or GIS field (mostly based on statistical models with few categorical variables per node), the approach here deploys a more accurate and more configurable model. Compared to models for operational building energy management (typically based on real time embedded simulation), the approach uses a lightweight, more flexible approach that avoids intensive simulation. The energy performance quantification of buildings, energy supply nd energy generation systems bring rich information to decision makers who will be well-positioned when they seek reductions in primary energy consumption and greenhouse gas (GHG) emissions. The model helps energy efficient system design based on system-wide outcomes, consequently achieving energy savings in the building sector and avoiding negative environmental impacts. A major benefit resulting from the research is that it has the capability to support decision making in large-scale building sector energy policy planning, i. e. beyond campus scale such as on a metropolitan scale. (Abstract shortened by UMI. )