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Energy Procedia 00 (2017) 000–000

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Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

The 15th International Symposium on District Heating and Cooling

Assessing the feasibility of using the heat demand-outdoor

temperature function for a long-term district heat demand forecast

I. Andrić

a,b,c

_{*, A. Pina}

a

_{, P. Ferrão}

a

_{, J. Fournier}

b

_{., B. Lacarrière}

c

_{, O. Le Corre}

c a_{IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal}

b_{Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France}

c_{Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France}

Abstract

District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period.

The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors.

The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

Keywords: Heat demand; Forecast; Climate change

Energy Procedia 158 (2019) 3026–3031

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.981

10.1016/j.egypro.2019.01.981

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy.

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significant interest in exploring the synergies between energy networks (e.g. power to gas energy storage and thermal storage providing demand response). Interactions take place through the conversion of energy between different energy carriers and its storage in order to provide services and ensure that each is operated in an optimal way [1]. This integration requires more flexibility in the entire energy system while ensuring security of supply, and it will challenge the existing energy (electricity, heating, transportation and gas) infrastructure and its control systems with more complicated dynamics and uncertain problems [2].

Energy consumption in buildings represents over 30% of society’s energy demand and half of global electricity demand [3]. As part of the transition to a sustainable and integrated future energy system, on one hand, it is of extreme importance to improve buildings’ energy efficiency; on the other hand, as shown in Fig.1, thermal capacity of the buildings can be used to become a flexible energy (both electricity and heating/cooling) consumer that can actively take part in the future smart energy systems by providing the demand side flexibility/ancillary services via the flexibility/electricity market (e.g. FLECH) for the transmission system operator (TSO), distribution system operator (DSO), district heating operator (DHO) and balancing responsible party (BRP), etc. [5].

Model predictive control (MPC) is seen as one of the key future enablers for an intelligent energy management in buildings to meet inhabitants’ comfort needs in a more efficient way [7]. This control technique consists in an on-line predictive optimal control of the energy system according to the estimated future behavior of the system given a number of disturbances (e.g. weather, dynamic energy price, occupancy…) [8][9]. The energy management decision is supported by a variety of parameters: energy consumption, dynamic energy price, share of renewable energy sources (RES) in generating mix, CO2 intensity of the power, and the deviation from an indoor temperature reference. Each of these parameter results in a different formulation of the control strategy. As highlighted in [10], trade-offs arise as part of the selection of the strategy, for example, to minimize energy consumption, to provide wind balancing service for BRP; or to reduce CO2 emissions, etc.

The remainder of the paper is organized as follows: what is MPC-based building energy management systems (BEMS) for demand response, its pros and cons are introduced in section 2. A case study of MPC-based BEMS for a multi-family residential building in Copenhagen is conducted with a hierarchy controller design, followed by a discussion of its implementation feasibility and practical barriers in section 3. The conclusion is drawn in section 4, together with a description of future work.

2. Current status of MPC-based BEMS for demand response (DR) 2.1. What is MPC-based BEMS for DR?

Model Predictive Control (MPC) is a control algorithm that optimizes a sequence of manipulated variable adjustments over a prediction horizon by utilizing a process model to optimize forecasts of process behavior based on a linear or quadratic objective, which is subjected to equality or inequality constraints [10]. In MPC-based BEMS, during each sampling interval, a finite horizon (e.g. every 15 minutes) optimal control problem is formulated and solved over a finite future window (e.g. next 36 hours)[11]. The result is a trajectory of inputs and states into the

Fig.1. Enabling the use of demand side flexibility for smart

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