# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 1 Introduction  
Energy transitions are gaining momentum with the digitalization of power systems and the use of smart grid technologies, which enable various possibilities to efficiently integrate renewable energy sources within the residential sector. In 2011, the European Commission acknowledged the importance of promoting smart grids and smart metering throughout the EU in their communication \"Smart Grids: from innovation to deployment\" [1]. Various benefits were highlighted: empowering consumers to directly control and manage consumption patterns [2, 3], enabling time-dependent electricity prices, providing more cost-efficient energy use, enhancing security of the grid [4, 5] enabling integration of renewable energy [6] and electric vehicles (EV) [7], boosting future competitiveness and technological leadership, and providing a platform for the development of innovative energy services from individual users and through aggregator companies [8, 9, 10]. The translation of these theoretical benefits towards practical applications has led to the implementation of many smart grid pilots within the EU [11]. According to the records of theEuropean Joint Research Center (JRC), a total of 527 smart grid pilots have been implemented in Europe, mostly categorized as Demonstration projects and Research and Development (R&D) projects. The total budget for these projects is 360 billion euro, with almost two third of the funds attributed to demonstration projects [12]. Another important parameter is the density of the investments. Namely, the Netherlands is cited as a high density spot in many reports [11; 13]. The Dutch government has put emphasis on the importance of smart grid pilots [14; 15; 16] and integration of renewable energies [17; 18]. Thanks to these policies, many differently configured energy systems have been implemented and tested in several residential smart grid pilots and demonstration projects, which are further elaborated in Section 2. In a conventional power grid, electricity consumption patterns of residential households(HH.) depend on geography, socio-economic factors of the households, and especially the number of residents per household, which we refer to as household size [19; 20]. In a residential smart grid, the net electricity that is imported from the grid (called import in this paper) is not equal anymore to the consumption, and varies depending on additional local power production by distributed energy resources (DER) coupled with home energy management software (HEMS) [21]. This mainly entails rooftop photovoltaic (PV) systems [22], but also other DER as micro-combined heat and power (\(\mu\)-CHP) [23]. Out of 84 projects executed in the Netherlands, 31 residential smart grid demonstration pilots involved more than 15 households per pilot and are well documented [24]. However, little information is available about the functioning of these new energy systems and their overall performances in combination with each other, as most studies tend to focus only on one aspect of electric load, which still contains a significant amount of valuable knowledge [25]. Therefore, this study analyzes four datasets in further detail, focusing on seven key performance indicators: (1) monthly electricity consumption and production; (2) annually imported and exported electricity; (3) residual load; (4) peak of imported and exported electricity; (5) simultaneity in import and feed-in; (6) and self-sufficiency and (7) self-consumption. These key performance indicators (KPI) are related to the performance of energy technologies [26] and to the end-users (prosumers) in residential smart grid pilots [27; 28]. These key energy performance indicators can be used to determine and quantify various aspects, namely, seasonal effects, extreme conditions during peak times and necessary import capacity and the degree of integration of renewable energy sources [26; 29; 30]. The annually imported electricity gives an indication of energy efficiency and provides insight into how much electricity must be generated.