Modelling and flexible predictive control of buildings space-heating demand in district heating systems

Abstract

This paper presents and demonstrates, by numerical simulation, a mixed-integer linear programming (MILP)-based Model Predictive Control (MPC) strategy for space-heating demand in buildings connected to a district heating system. The proposed MPC deals with space-heating demand with extended flexibility. It exploits thermal inertia, inherently present in the building and its heating system, to optimally plan space-heating load in anticipation of weather conditions and energy cost variations. MPC is based on a reliable Reduced-Order Model (ROM). Heating circuit and internal mass are carefully modelled within the ROM structure since these elements can be used for short-term heat storage and therefore play an important role in demand-side management. As for the model parameters identification, training data is restricted to non-intrusive, easily accessible measurements available at the substation level. The model identification approach and control strategy are applied to a well-insulated radiator-heated case-study building simulator developed in Modelica. Results show that the proposed ROM is reliable enough for an MPC application. Compared to conventional weather-compensation control, flexible MILP-based MPC proved to be cost-efficient, while preserving a decent indoor thermal comfort level.

Techno‐economic‐environmental analysis of a smart multi‐energy grid utilising geothermal energy storage for meeting heat demand

“…Among the papers without modelling GES [18][19][20][21]: (i) Technical [19] and techno-economic (TE) [18] analysis of several scenarios for different shares of integrated gas and electricity transmission networks (IGETNs) to meet the heat load have been investigated; (ii) Technical analysis of gas boiler (GB)-or CHP-driven DHN at the distribution level was studied in [20]; and (iii) TEE analysis of combinations of GB-, CHP-or HP-driven DHN for district heat loads and GB, HP or CHP to meet the rest of the heat loads (called 'local' heat load in their paper) has been investigated at distribution level [21].…”

Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems

“…The electricity price used was based on Octopus energy's tracker tariff, which follows the electricity wholesale market price. The Nord-Pool power market spot prices were used for the wholesale electricity price (again from 1-31 st December 2017) [39] and the following equation is used by Octopus Energy to calculate the electricity price sent to consumers in London [40]: c e = 1.19c e,W HS + 0.006787 (14) where c e is the electricity price [£/kWh] and the WHS subscript denotes the wholesale price. The temperature setpoints used in each flat were created for the Integrated Electric Heating Project [41] and represent the average temperature profiles in dwellings with different types of occupants.…”

“…This has resulted in a significant number of MPC-based studies for district heating. Application of the concept to the district heating domain is described in [13], while specific recent studies include the the Mixed-Integer formulation in [14] and a formulation centred on the incorporation of Thermal Energy Storage (TES) in [15]. The benefits of MPC enables heating efficiencies in the building sector to be improved while potentially providing optimal coordination for the benefits of the wider energy systems in an urban setting.…”

Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building

“…In this regard, several types of MPC algorithms have been applied to HVAC systems in the literature [13][14][15][16][17][18][19][20][21][22][23][24][25][26]; see also [27,28] and the references therein. In particular, [15] presents two stochastic MPC algorithms, i.e.…”