**1. Introduction**

The European Union agrees on drastically lowering CO2 emissions in order to mitigate the e ffects of climate change [1,2]. Currently, in the Netherlands, electricity generation is mostly achieved by means of fossil fuels and is responsible for a significant portion of the total emissions [3]. To meet European targets, a transition to more sustainable energy generation is necessary within the country to decarbonize the grid [4,5]. When transitioning towards a low-carbon society, not only sustainable generation, but also energy saving on the demand side is even more important [6,7]. Transport and the built environment account for approximately 24% and 36% of total energy consumption in the Netherlands and are therefore responsible for much of the emissions due to fossil fuels [3,8]. It is evident that an e ffective transition to a sustainable future also requires technologies on the demand side [9,10] that can be powered by Renewable Energy Sources (RES), such as electric heat pumps [11,12] to fulfill the heating demand of buildings and electric vehicles for transport [13], in the Dutch context [4].

A transition to more sustainable energy generation is expected to bring about a variety of challenges. Firstly, the foreseen large-scale deployment of RESs may seriously a ffect the stability of energy grids [14,15]. An increase in power grid-connected RESs results in a change in power generation characteristics and grid operation [16]. In contrast to conventional fossil power plants, RESs are often relatively small power generators and are distributed throughout the low- and medium-voltage grid levels [17]. When RESs are integrated into the built environment, buildings will both consume and supply energy to the grid and become active 'prosumers' [18]. This creates multi-directional

energy flows on the low- and medium-voltage grid levels [19]. Additionally, through the continuing electrification of space heating by heat pumps and transport by electric vehicles, the pressure on the transmission and distribution grids will increase further, thereby increasing the risk of congestion [20,21].

A specific problem that can be encountered is "overgeneration," with increasing penetration of solar photovoltaics (PVs). The Californian Independent System Operator published a "duck chart" which shows during springtime a significant drop in midday net load as more PVs are added to the system [22]. This introduces a huge problem in ramping up the generation, as PV power production rapidly decreases as the sun sets in the evening. Notably, no research reports, papers, or other documents were found which describe the duck curve or a similar problem explicitly in the Dutch context. However, a quick analysis of the installed PV capacity growth over the years and the grid loads in the Netherlands indicate that the problem increases as the current PV growth trend progresses. Large-scale integration of PV generation could also lead to local problems. The decentralized generation of PV power could lead to overvoltage and congestion in the low-voltage grid level when there is high PV power generation but low demand [23].

All the aforementioned problems call for more intelligent ways of consuming electricity. One possible way is a Smart Grid [24], where both demand and local production in the distribution grid are controlled in order to stabilize the grid [14]. Many definitions of a Smart Grid exist [25]. According to the Institute of Electrical and Electronics Engineers (IEEE) [26], the Smart Grid has come to describe a *next-generation electrical power system that is typified by the increased use of information and communication technology (ICT) in the generation, delivery and consumption of electrical energy*. The future power grid is expected to provide unprecedented flexibility in how energy is generated, distributed and managed [27]. The Dutch branch organization of energy network operators (Netbeheer Nederland) estimates that the total need for flexibility in the Netherlands will double towards 2030 compared to 2015, and increase even further by a factor of three towards 2050 [28].

Power system flexibility can be achieved through a variety of di fferent interventions at both the supply and demand side [29]. The traditional approach is supply-side flexibility, which could be delivered by supply-side energy storage, power plant response, curtailment of variable renewable electricity generators or dedicated power plants such as combined heat and power (CHP) and combined cycle gas turbines [29]. The demand side, which includes the built environment, can also adapt its electricity demand according to grid needs through the adoption of Demand Side Management (DSM) programs. Gellings [30] describes DSM as: "*the planning and implementation of those electric utility activities designed to influence customer uses of electricity in ways that will produce desired changes in the utility's load shape*". Techniques such as peak shaving and valley filling could be used to accomplish the load shape objective [31], especially with the use of storage systems. The built environment could thereby provide energy flexibility, which is defined by the International Energy Agency (IEA) Annex 67 as *a building's ability to manage its demand and generation according to local climate conditions, user needs and grid requirements* [32].

Building energy flexibility is not just limited to the utilization of storage systems but also energy systems inside the buildings to create a balance with the building-integrated renewable energy production. Of the energy systems present in a commercial o ffice building, Heating Ventilation and Air Conditioning (HVAC) [33] systems account for approximately 63% of the energy requirements (in the Netherlands in 2017) [34]. Therefore, the employment of HVAC systems for realizing Demand-Side Flexibility (DSF) services is interested and driven by the following factors [19]:


The significant energy demands and the aforementioned control advantages allow HVAC systems and energy storage systems to provide effective energy flexibility and managemen<sup>t</sup> services for the built environment. Buildings could, therefore, offer DSF by manipulating installations to respond to power system requirements by increasing or reducing electricity consumption patterns while maintaining a comfortable and productive environment for the occupants [19]. Additional DSF could also be delivered through the control of lighting and plug loads [35].

To provide the above-mentioned decision-making requirement, it is necessary to perform accurate short-term and small-scale electricity load forecasting on subsystem levels of individual buildings [36]. The energy behavior of a building is influenced by many factors, such as weather conditions, building construction, the thermal properties of the building, the occupancy, and occupant behavior [37]. Forecasting subsystem-level loads is therefore considered a complex and challenging problem [36]. However, this type of demand prediction could be a valuable contribution to maintaining a reliable electricity grid.

Contributing to solving the mentioned problems, the objective of this research is to identify and implement building energy managemen<sup>t</sup> opportunities using subsystem-level electricity demand prediction and a Battery Electric Storage System (BESS). The objective of this study is to stabilize/flatten a building energy demand profile to demonstrate energy flexibility for a future Smart Grid without compromising user comfort. The proposed methodology in Section 2 is advantageous to the research community because it discusses the demand prediction of several subcomponents (AHU, HVAC, chiller, lighting, and plug loads) of buildings, which is otherwise rare in the existing literature. Moreover, a major contribution would be the implementation of the discussed methodology in a real-life office building. Next, the quantitative results and qualitative findings have been presented in Sections 3 and 4.
