Smart Appliances for Efficient Integration of Solar Energy
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applied sciences Article Smart Appliances for Efficient Integration of Solar Energy: A Dutch Case Study of a Residential Smart Grid Pilot Cihan Gercek 1,* and Angèle Reinders 1,2 1 Department of Design, Production and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands; [email protected] 2 Energy Technology Group at Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands * Correspondence: [email protected]; Tel.: +31-534897875 Received: 7 December 2018; Accepted: 28 January 2019; Published: 10 February 2019 Abstract: This paper analyzes the use patterns of a residential smart grid pilot in the Netherlands, called PowerMatching City. The analysis is based on detailed monitoring data measured at 5-min intervals for the year 2012, originating from this pilot which was realized in 2007 in Groningen, Netherlands. In this pilot, smart appliances, heat pumps, micro-combined heat and power (µ-CHP), and solar photovoltaic (PV) systems have been installed to evaluate their efficiency, their ability to reduce peak electricity purchase, and their effects on self-sufficiency and on the local use of solar electricity. As a result of the evaluation, diverse yearly and weekly indicators have been determined, such as electricity purchase and delivery, solar production, flexible generation, and load. Depending on the household configuration, up to 40% of self-sufficiency is achieved on an annual average basis, and 14.4% of the total consumption were flexible. In general, we can conclude that micro-CHP contributed to keep purchase from the grid relatively constant throughout the seasons. Adding to that, smart appliances significantly contributed to load shifting in peak times. It is recommended that similar evaluations will be conducted in other smart grid pilots to statistically enhance insights in the functioning of residential smart grids. Keywords: smart grids; renewable energy; flexibility; demand shifting; photovoltaic systems; smart appliances 1. Introduction Residential photovoltaic (PV) installations are one of the promising options to locally generate and consume sustainable and cost-effective energy [1]. One of the major technical issues related to the integration of renewable energy systems into local electricity networks is balancing the mismatch between demand and supply of power [2]. Daily and seasonable meteorological conditions significantly affect renewable energy production [3] as well as demand patterns. 100% matching of the residential consumption with renewable energy can be achieved by PV systems in combination with residential storage systems such as batteries [4,5], vehicle to grid technologies [6], or by using community-based storage systems [7]. Although batteries may be required to maintain a high quality of the power fed into local electricity networks, alternative solutions for the realization of flexibility may need to be evaluated because of the high environmental impact of batteries [8]. For instance, one can think about other types of storage systems or optimizing the capacity of batteries. Rather than self-consumption with flexible loads or temporary storage of the PV infeed to the grid, an efficient and sustainable integration of renewable energy is only possible if the network is flexible and resilient [9]. Electric flexibility can be defined as a power adjustment sustained at a given Appl. Sci. 2019, 9, 581; doi:10.3390/app9030581 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 581 2 of 14 moment for a given duration from a specific location within the network [10,11]. Thus, a flexibility service is characterized by five attributes: its direction, its electrical composition in power, its temporal characteristics defined by its starting time and duration, and its base for location [10]. To enable all the Appl. Sci. 2018, 8, x FOR PEER REVIEW 3 of 15 potential flexibility, the organization and functioning of electricity grids will require more intelligence and complexity,quantified to forput whichthis analysis reason in theya more are complete called ‘smart perspective. grids’. In Smart recent gridsEU reports, balance one variations of the most of the energydense production areas in terms in renewable of the investment energies in withsmart regards grids was to the energy Netherlands, demand and and the regulate peak year the of demandthe side via,investment for instance, was 2012 shiftable [26]. The loads investigated with respect final phase to time (phase and 2) quantity of the pilot [12 duration]. is January 2012 throughFirstly, sustainableJanuary 2015, supply and the flexibility available data might for be our offered research by was the limited network to January itself through 2012 until storage systemsJanuary (hydroelectricity, 2013. fuel cells, and hydrogen). Hydrogen technologies and fuel cell–powered This paper is structured as follows. The pilot configuration, energy tariffs, the data, the data electric vehicles may provide a balanced energy system [13]; however, such systems are still quite processing methods, data quality, and equations applied to determine energy indicators are expensivepresented [14 ].in Section Therefore, 2. The one results of are the presented most promising in Section solutions3 and discussed to increase in Section flexibility 4. The paper is the is use of combinedcompleted distributed by conclusions energy presented resources in Section (DER), 5. such that they will jointly produce electricity on moments of demand. In this scope, micro-combined heat and power (µ-CHP) units could be complementary2. Materials and to residential Methods PV systems by offering both electricity and heating, especially if the electricity prices are fairly high or natural gas prices are relatively low [15,16]. Therefore, we would like to2.1. evaluate PMC Configuration the efficient integration of PV systems into a local network that comprises different configurationsMain features of DER. of PMC households are summarized in Table 1 [24,27]. Ten out of twenty‐two householdsSecondly, owned residential a μ‐CHP homes unit with with a nominal various power smart of 1 appliances kW of electrical may energy. contribute Adding to to that, the load flexibilitya μ‐CHP [17 ]unit together was able with to produce home energy 6 kW thermal management energy systems to heat the and house, demand using response a hot water [18 buffer,19]. In the literatureof 210 [ 20L. ],Four domestic of the households cleaning practices had rooftop by PV use installations of smart washing with an machinesarea and nominal and smart power dishwashers given in Table 1. Through the local smart grid, 18 other households virtually shared a PV system on a farm are described as the most favorable residential consumption practices for demand side response [21]. located 2.3 km from Groningen, the actual location where this smart gird pilot is installed. Each Heating and lighting practices have a medium flexibility potential, according to the same social household received a nominal power of 1590 Wp from this farm. study. Moreover,The smart in appliances terms of theinstalled price in responsiveness this pilot were ofsmart electricity washing users, machines, dishwashers smart dishwashers, are qualified as significantand smart drivers hybrid in heat time-of-use pumps (SHHP) tariffs [with22]. Bya condensing means of boiler. these smartThe SHHPs appliances, contained this heat study pump aims to evaluateunits the with flexible a thermal load power in a output smart gridof 4.5 pilot, kW and particularly a condensing its boiler temporal with characteristicsa thermal power and output average electricalof 20 compositionkW. Additionally, in power. a 210‐L hot water buffer was used in these systems. In this pilot, the smart washingIn this paper, machines flexibility and smart in both dishwashers supply andwere demand programmable is analyzed by time by so means that users of detailed could program monitoring datait of to PowerMatching their needs or comfort City (PMC),expectancies. which Half is a of residential the households smart had grid been pilot equipped which with got realizedthese two in the yearsmart 2007 in appliances. the City of The Groningen expected outcome in the Netherlands of experiments [23 ].with This PMC pilot household includes smart 22 households energy systems (HH) with matched the time of use of smart appliances with PV power production by a smart algorithm. Figure PV systems and different configurations of their energy systems with µ-CHP, smart hybrid heat pumps 1b shows the household consumption and production of a PMC household equipped with PV and (SHHP),μ‐CHP and in also the smartwinter, appliances, when irradiance as illustrated levels are in low. Figure It gives1a and an example detailed of in how Table PV1[ 24and]. μ‐ AnCHP energy managementjointly produce software, electricity PowerMatcher, to meet the demand, has been aiming used to at operateminimizing power the power flows flow on this from pilot the [grid.25]. (a) (b) FigureFigure 1. PowerMatching 1. PowerMatching City: City: ( a()a) scheme scheme of the system, system, (b ()b energy) energy consumption consumption (blue (blue line), line), energy energy productionproduction (red (red line), line), and and power power flow flow (blue (blue area)area) for a a household household with with photovoltaic photovoltaic (PV) (PV) systems systems and and Micro-combinedMicro‐combined heat heat and and power power ( µ(μCHP)CHP) jointlyjointly producing producing energy,