MIP approach for designing heating systems in residential buildings and neighbourhoods

Abstract

In this work, a mixed integer linear programming (MIP) approach for the optimal design of energy systems in residential buildings is presented. The optimization model considers the economic criteria of the guideline VDI 2067. The objective of the MIP is to minimize the annual costs which comprise the investment as well as demand-and operation-related costs. Conventional boilers, electrical heaters, combined heat and power (CHP) units, heat pumps (HPs), photovoltaic (PV) systems and thermal storages as well as local heating networks (HNs) are defined as options. The investigation on a building level shows that a boiler is the economically optimal solution for small buildings, followed by a HP unit. In multi-family buildings, both boilers and CHP units hold an economical advantage over HPs. For apartment buildings, CHP is identified as the economically optimal system. In the neighbourhood analysis of six buildings, the solver establishes a local HN which allows for both economical and CO 2 -emission reductions.

A Review on Time Series Aggregation Methods for Energy System Models

“…In contrast to that method, one TD for each month at hourly resolution, resulting in 288 time steps, was used by Mavrotas et al [54], Lozano et al [99], Schütz et al [100], and Harb et al [101]. Although thermal storage systems have been considered in the literature [99][100][101] (as well as a battery storage by Schütz et al [100]), they were constrained to the same state of charge at the beginning and end of each day. The same holds true in the work of Kotzur et al [57].…”

An optimisation framework for thermal energy storage integration in a residential heat pump heating system

“…14. For a heat pump heating system, the temperature increase in the storage tank, ∆T T ES is set to 10 K [16].…”

“…Furthermore, they can be difficult to set up which prevents their widespread use. On the other hand, the second approach involves modelling reduced complexity models of the energy systems using mathematical programming methods, for example evolutionary algorithms [12,13] or mixed integer linear programming (MILP) [14,15,16]. These studies typically include sizing and operational optimisation of various potential generation and storage equipment to fulfil the given energy demand.…”

“…Unfortunately, measured demand profiles are usually not available and thus synthetic demand profiles are generated and utilised as an input. Some studies use building simulation software [11] or multi-purpose simulation tools [16] which require a large number of input parameters and are not very accessible. Other studies use relatively simple correlations between demand and external temperature but neglect the occupancy profile [17].…”

Lifecycle cost and CO2 emissions of residential heat and electricity prosumers in Finland and the Netherlands

“…The authors identified the optimal solutions in terms of cost and environmental benefits, as well as the effect of including PV panels in the system. The mixed integer linear programming approach by Harb et al [14] showed that the optimal design and operation strategy of energy systems depends on the type of residential building. In their study, they found that boilers in combination with PV are preferable for single-family houses, while combined heat and power (CHP) and local heating networks are preferable for larger buildings and neighborhoods, respectively.…”