Demand side flexibility: Potentials and building performance implications
Graphical abstract
Introduction
Increased decentralized renewable energy production at the low voltage levels has led to the transformation of electricity supply chain infrastructure and consequently heralded development of smart grids concept. A smart grid is defined as an upgradable electricity network often occurring at the low voltage region of distribution and that is enabled for intelligent control and multi-directional communication between sources, loads and components in such a manner that allows for cooperative and economical energy utilization (Farhangi, 2010, Giordano and Fulli, 2011). The smart grid is about electrical connectivity to active loads and generators within an elaborate demand side management programme effected with the aid of elaborate communication infrastructure, sensor network, automated metering, demand driven control systems and intelligent coordination (Siano, 2014). The emergence of the smart grid concept is partially related to an attempt to solve the challenge posed by integration of renewable energy sources (RES) in form of associated uncertainties (Sartori, Napolitano, & Voss, 2012). Central to this is the suggestion to use additional power flexibility in current electricity power grid as a way of mitigating the uncertainties (Lund, Lindgren, Mikkola, & Salpakari, 2015). The term flexibility is described in power systems engineering as the ability to cost effectively balance electricity supply and demand continually while also maintaining acceptable service quality to connected loads (Ulbig & Andersson, 2015; Cochran et al., 2014). This definition is inclusive of the ability for periodic energy availability to the grid over a defined time, response to random unscheduled load and provision of additional reserves to manage uncertainties arising from inaccurate forecasting or sudden change in the weather (Olsen et al., 2014, Hummon et al., 2014). As illustrated in Fig. 1, flexibility sources in the electrical power grid can be grouped under two categories (Lund et al., 2015, Cochran et al., 2014):
- a.
Supply side flexibility—in the form of dynamically fast responding conventional power plants, combined heat and power plants, combined gas turbine cycles plant, large scale storage systems, and curtailment of RES power feed-in,
- b.
Demand side flexibility (DSF)—in the form of services delivered through intelligent integration of connected loads in carefully crafted demand-side management (DSM) programs. Applied to this context, intelligent integration implies adaptation of present electricity supply chain infrastructure to improve flexibility, agility, and responsiveness to frequent changes (Vojdani, 2008).
Supply side flexibility in this context refers to dedicated power plants or supply side storage often using fossil fuel and having capacity to make up for the mismatch between electricity generation and consumption by ramping up or down in required time and duration (Cochran et al., 2014). Due to the high cost of operating and maintaining flexibility sources on supply-side and increased localized power networks reliability related issues, emphasis has of recent been focused on alternative cost effective DSF strategies such as demand response and demand driven control measures (Xue et al., 2015, Labeodan et al., 2015a). DSF is the ability of demand side installations to respond to power systems requirements for ramping up or down using on-site storage capabilities, increasing or reducing electricity consumption patterns whilst maintaining acceptable indoor comfort bandwidth (Zheng et al., 2015, Iwafune et al., 2015). Load classification details for DSF within DSM framework is available in Fig. 1a.
DSM is inclusive of all undertakings on the demand side of an energy system undertaken in close collaboration of the consumers and power system utilities in efforts to alter load pattern using incentives, subsidies or cash benefits (Palensky, Member, Dietrich, & Member, 2011).
As shown in Fig. 1b, DSM whether residential, non-residential or industrial based can be classified with respect to strategy employed as either incentive based (also referred to as reliability based) or price based (also referred to as economic based)(Lund et al., 2015, Palensky et al., 2011, Müller et al., 2015). Lund et al. (2015), Palensky et al. (2011) and Müller et al. (2015) all describe incentive type DSM as those generated by deterioration in overall power quality through regulation based services whereas price based DSM are those that are generated by end-user desire to reduce cost of electricity. DSM for residential sector is often centred around load shifting and peak reduction by ensuring that household tasks are undertaken with respect to prevailing electricity prices; these are mostly price based and thrive on existing diversity of tasks and appliances that can be undertaken on rotational basis. On the other hand, non-residential buildings such as offices have fairly fixed schedules with little room for manoeuvre as all the tasks have to be undertaken at the same time. Common office building loads include those dedicated to provision of space cooling, space heating, humidification, ventilation, office appliances’ operation and lighting. For office buildings, labour is more productive than energy cost thus making DSM practices more incentive based than price based. Ultimately, DSM involving non-residential systems tends to rely on strategies incorporating storage system use or system inertia use or cooperative energy usage with neighbourhood buildings.
With respect to power system flexibility, useful strategies may be those for incorporating direct market participation, direct load control, real time period and spinning reserve; these generally fall under the category of demand response or demand based control services. Increased depth in the application of DSM especially with regards to the ability for regulation, spinning reserve and differentiation as an energy market service has been possible mainly because of successful innovation and end user incentives involving renewable energy technologies and improvements in information and communication technologies (Siano & Sarno, 2016). As regards this, Siano (2014) envisages that demand response will become crucial for peak load management and frequency regulations with faster response times and as such is a crucial part of smart grid implementations.
Buildings form an essential part of the smart grid due to the significant energy consumed and produced in them. It is reported that buildings (both residential and non-residential) account for between 20 and 40% of the total final energy consumption (Pérez-Lombard, Ortiz, & Pout, 2008); in Europe this figure is reportedly 40% with electricity accounting for over 48% of the total final energy mix (Economidou, Laustsen, Ruyssevelt, & Staniaszek, 2011). By virtue of this, buildings form a potential source of worthwhile DSF.
The purpose of this article is to analyze the performance of office buildings when used within the context of electricity demand-side management (DSM) programs to provide power systems flexibility services to the smart grid. The specific focus is therefore on the performance of office buildings during incentive based DSF activities.
The paper makes two main specific contributions.
- 1.
First, it outlines building performance implications when operating them as demand-side flexibility resources in a building centric format with emphasis on thermal comfort, indoor air quality, comfort recovery period and comfort systems response. Past studies have often concentrated on potential evaluation without much regard for building performance issues; these types of studies have often over-simplified building performance issues and instead present evaluations that are top-sided with power performance characteristics. This is despite results from a numerical study by Morales-Valdés, Flores-Tlacuahuac, and Zavala (2014) which indicated that comfort relaxation strategy could result to higher levels of percentage people dissatisfied (PDD) with indoor comfort. Another study by Zhang and de Dear (2015) also revealed that for majority of cases where direct load control strategies were applied, adverse thermal comfort impacts on the occupants were prevalent.
- 2.
Second, it uses actual data derived from field studies undertaken in an average-sized office building to evaluate its potentials for demand-side flexibility rather than simulations as is the norm with past studies as evidenced in Olsen et al. (2014), Hummon et al. (2014) amongst others. This eliminates the bias arising from general assumptions often associated with numerical analysis in attempts to simplify complex scenarios. This study is hence crucial; taken that despite the noted viability for demand-side flexibility from office buildings very few empirical studies have been conducted on the subject with even fewer delving on the practical implication to building performance.
To buttress associated contributions and rationale, a review of key studies in demand-side flexibility with emphasis to office buildings is presented in Section 2.
Section snippets
Demand side flexibility studies
The idea of using buildings as a source of power systems flexibility is fairly recent. In a pioneer study, Rosso, Ma, Kirschen, and Ochoa (2011) proposed a framework for optimizing the generation portfolio that considered both operational and investment costs in addition to using demand side for power grid flexibility. The framework presented was empirically evaluated for a scenario in Scotland which assumed that 5% of the demand was available as power flexibility; the evaluation used actual
Concept and investigation strategy
The study adopts a key tenet that indoor comfort in buildings exists within allowable range of design guidelines. Thus, for all comfort states S there exists a maximum bundle of comfort given by the superior limit of the parameters at state ‘Smax’ and also a minimum bundle of comfort given by the inferior limit of parameters at a state ‘Smin’. These comfort bundles are assumed to be achievable through operation of flexible loads. A flexible load in this context is defined as a connected
Results
Results confirmed the potential of using the two DSF strategies investigated. Table 6 outlines summarized results with respect to prevailing operative temperature, carbon dioxide concentration, PPD, availability period, comfort systems response time, comfort recovery and demand reduction as a result of operating on DSF mode.
Further details of results from the experiments are discussed in Section 4.1 (for ventilation experiment) and 4.2 (for cooling system experiment)
Discussion of results-ventilation experiments
The response time of the air supply fan of less than 1 min makes it ideal for use as a the initial resource in case of DSF service. This conforms with past findings indicted in Table 1 that air supply fans of air handling units in buildings are fast responding.
However, with respect to experimental results from ventilation experiments, differential IAQ within the building must be managed and availability of demand side flexibility capability determined reliably.
Conclusions
The need for power systems flexibility has given a new breadth of life to DSM. In this context, buildings’ based DSF is undoubtedly critical for the well-being of the new electricity supply chain infrastructure. For an average sized office building, this study has confirmed the DSF potential with respect to the following strategies:
- 1.
cyclical operation of installed centralized air-supply fan at reduced PID settings, and
- 2.
cyclical operation of installed water to air cooling system using a re-set
Acknowledgement
The support of Kropman Installatietechniek, Almende, CWI and Rijksdienst voor Ondernemend Nederland is acknowledged in realizing this study.
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