Energy Institute Research Collection

The prediction of water consumption patterns is a challenge, especially when water metering is not available at scale. The paper focuses on the prediction of analytical domestic hot water (DHW) demand profiles for detailed building archetype models, using an occupant focused approach based on time-of-use survey (TUS) data. Five dwelling types are considered over different construction periods, representative of the majority of the Irish residential stock, which is used here as a case study. They are modelled at room level using EnergyPlus and converted into archetype models. A bottom-up approach is utilised to develop the required operational data at high space and time resolution. That methodology applies Markov Chain Monte Carlo techniques to TUS activity data to develop activity-specific profiles for occupancy and domestic equipment electricity use. It is extended to DHW demand profiles by combining the probability distributions for particular TUS activities with average daily DHW consumptions, depending on the household size, day type and season. The archetype models are found to be 90% accurate with the Irish standard dwelling energy assessment procedure in estimating the annual energy requirements for DHW heating. Moreover, they capture variations in DHW consumption, heat demand and energy usage for DHW heating, on a national scale and a fifteen-minute basis.

With the increasing share of renewable energy sources (RES) in demand supply, the distribution network operators (DNOs) are facing with new challenges. In one hand, it is desirable to increase the ability of the network in absorbing more renewable power generation units (or increasing the hosting capacity (HC)). On the other hand,  power injection to the distribution network by renewable resources may increase the active power losses (if not properly allocated) which reduces the efficiency of the network. Thus, the DNO should make a balance between these two incommensurate objective functions. The Demand Response (DR) in context of smart grids can be used by DNO to facilitate this action. This paper provides an approach in which a multi-objective and multi-period NLP optimization model is formulated where the DR is utilized as an effective tool to increase HC and decrease the energy losses simultaneously. In order to quantify the benefits of the proposed method, it is applied on a 69-bus distribution network. The numerical results substantiate that the proposed approach gives optimal locations and capacity of RES, as well as minimum energy losses by load shifting capability provided via DR programs.

Renewables are increasingly replacing power from conventional generators. Renewable power injected through power electronic converters lacks the fundamental electric torque components. Electric torque components have an important role in determining the behavior of conventional machines in the network. The influence of this factor becomes more notable in power systems with reduced inertia. Hence, questions arise on, how can synchronizing torque basically contribute to the rotor speed deviation and eventually the system frequency and if there is a potential for using the steady state synchronizing torque coefficient (STC) to achieve acceptable frequency operating points. This paper calculates the steady state STC matrix by using the multi-machine Heffron-Philips model in conjunction with the network admitance matrix. Accordingly, it investigates the impact of the generator location and reactive power output on the STC matrix. It demonstrates how this impact manifests in the generator rotor speed deviation. Eventually, the significance of the STC from the system frequency perspective is assessed.

Detailed bottom-up load modelling of the residential sector has become increasingly important to examine the network impacts of both changing load composition due to the introduction of sustainable technologies, and changing load behaviour with increased levels of demand response. An important aspect of these models is the electrical and thermal behaviour of household loads. This paper examines the fundamental electrical and thermal characteristics of common household appliances. Methods to obtain the voltage de endency and equivalent resistive-capacitive (RC) circuit parameters for mode ling thermostatically controlled appliances (TCAs) are presented. The paper also presents the results of laboratory experimental determination of voltage dep endency coefficients, subjecting common appliances to a range of voltages within +/- 10% of the standard supply voltage. The thermal behaviour of TCAs are examined by use of thermocouples and plug-load monitoring devices. Appliances are grouped into into five distinct categories; lighting, motor, power electronic, resistive and wet appliance loads, and both their characteristics and operational behaviour is presented.

The continued penetration of renewable energy sources in electricity generation and the de-carbonization of the domestic space heating and hot water sectors is increasing the importance of demand side management (DSM). The development of end-use energy consumption models that can be easily integrated with electricity dispatch models is crucial for the assessment of the integration of supply and demand. The energy consumption of the domestic building stock is highly correlated with occupant behaviour, however the inclusion of occupant behaviour in energy models is challenging due to its highly variable nature. Nevertheless, in order to obtain reliable models of domestic energy consumption at high time resolution, the analysis of occupant behaviour patterns is fundamental. This paper aims to develop a new methodology to generate realistic occupancy patterns that can be representative of large numbers of households. This method is based on the clustering of household occupancy profiles using the UK 2000 Time Use Survey data as a case study. The occupancy profiles that result from this method can be used as input to residential building energy end-use models, thereby giving improved overall model performance.

Power network diagrams are typically neither enlightening nor attractive to look at. Encouragingly, though, the visualization of generic complex networks has been an active area of research for the past two decades, and there now exist a number of widely-deployed algorithms that show a network's structure in a revealing and aesthetic way. Additionally, recent work by the present authors has proposed techniques for diagramming power systems that explicitly use meaningful electrical distance metrics. Which is the most effective approach to diagramming? To begin to answer this question, this paper proposes new quality metrics for power system diagrams which seek to quantify how legibly a network layout reveals how power flows through it.

Increasing wind capacity integration results in displacement of active power from conventional generators and a reduction in reactive power sources available. As such, voltage stability may become a concern in certain periods for power system operation particularly in weaker areas of the network. Thus, it is of importance to consider the AC constraints for optimal wind generation planning (long term) in order to decrease the possibility of a wind capacity allocation that requires costly remedies from the power system operation perspective (short term). In this work, a procedure is proposed for wind capacity allocation with the aim of benefiting from the potential of an optimal wind capacity allocation for enhancing the voltage stability margin. The procedure is based on a multi operating conditions voltage stability constrained optimal power flow. The wind capacity target is set and the loadability margin is tracked. The results will show the applicability of the proposed procedure and will emphasize the effects of the pattern of wind capacity allocation on the loadability margin. This will result in a wind capacity allocation that enhances the minimum loadability margin among the possible future operating conditions considered for planning. The procedure uses the Maximin concept for this purpose.

Traditional building performance metrics consider a building as a standalone and static utility consumer. Voluntary green building certifications of districts generally aggregate the metrics of standalone and consuming buildings. There is a lack of performance metrics concerning the integration of critical services to a building and the utility networks supplying these critical services of electricity, natural gas and water. In order to achieve integration of energy systems, including storage based demand side management and rain water harvesting, a methodology is modelled for a typical office. The methodology requires building parameters to be combined and manipulated in order to create the proposed performance metrics. The building model is simulated for three periods of interest: a whole year, a winter design day, a summer design day. The proposed metrics enable operational management during peak and standard loads, as well as longer term analysis of the building performance. Operational management includes the role of storage and the responsiveness of a building during demand ramping or shedding. Over the longer term, the metrics indicate efficiency trends and guide design and investment decisions. It is found that electrical storage combined with demand side management reduces energy costs with no service disruptions. Rain water harvesting is also found to significantly reduce financial and energy costs, and given its current dearth of deployment, has high future potential.

This paper investigates demand response measures, using an EnergyPlus simulation model, developed specifically for demand response analysis, in a mixed-used commercial building. The effectiveness of various building pre-conditioning strategies, which were considered for different durations, immediacy and activation time were assessed using the simulation model. Assessment was carried out for a representative summer day and the contribution of the building capacitance as a mechanism for shifting the building electric power demand was evaluated, recording a maximum load reduction of 6.6% of the baseload.

Aims: The prediction of water consumption patterns is a challenge, especially when water metering is not available at scale. The use of time-of-use survey (TUS) data offers an alternative to metering in order to track the general patterns of water consumption across large and representative groups of end-users. The paper focuses on the prediction of analytical domestic hot water (DHW) demand profiles for detailed building archetype models, using an occupant focused approach based on TUS data. The paper illustrates and discusses the resulting capability of dwelling archetypes to capture variations in heat demand and energy usage for water heating on a national scale and at high time resolution. Methodology: Five dwelling types are considered over different construction periods, representative of the majority of the Irish residential stock, which is used here as a case study. They are modelled at room level using EnergyPlus and converted into archetype models. A bottom-up approach is utilised to develop the required operational data at high space and time resolution. That methodology applies Markov Chain Monte Carlo techniques to TUS activity data to develop activity-specific profiles for occupancy and domestic equipment electricity use. It is extended to DHW demand profiles by combining the probability distributions for particular TUS activities with average daily DHW consumptions, depending on the household size, day type and season. Results: The archetype models capture variations in DHW consumption, heat demand and energy usage for DHW heating, on a national scale and a fifteen-minute basis. Moreover, they are found to be 90% accurate with the Irish standard dwelling energy assessment procedure in estimating the annual energy requirements for DHW heating. Conclusion: This study demonstrates the potential for utilising time of use surveys to predict domestic water demand profiles on a national scale and at high time resolution.

With increasing penetrations of wind generation, based on power-electronic converters, power systems are transitioning away from well-understood synchronous generator-based systems, with growing implications for their stability. Issues of concern will vary with system size, wind penetration level, geographical distribution and turbine type, network topology, electricity market structure, unit commitment procedures, and other factors. However, variable-speed wind turbines, both onshore and connected offshore through DC grids, offer many control opportunities to either replace or enhance existing capabilities. Achieving a complete understanding of future stability issues, and ensuring the effectiveness of new measures and policies, is an iterative procedure involving portfolio development and flexibility assessment, generation cost simulations, load flow, and security analysis, in addition to the stability analysis itself, while being supported by field demonstrations and real-world model validation.

This paper develops an algorithm for energy management of a distribution network considering a restorative plan for most possible devastating contingencies in the network. The distribution network is selfhealing and consists of smart meters and remotely controlled automated switches. Distributed energy resources and flexible loads provide the redundant capacity for restoration. Under normal operating conditions, the objective of the energy management system is to minimize the generation cost, while under emergency conditions, the objective is to minimize the amount of shed load, giving priority to critical loads. The energy management problem forms a non-linear programming problem. Simulation results verify the effectiveness of the proposed algorithm in energy management and restoration of a distribution network.

The presence of uncertain parameters in electrical power systems presents an ongoing problem for system operators and other stakeholders when it comes to making decisions.Determining the most appropriate dispatch schedule or system configuration relies heavily on forecasts for a number of parameters such as demand, generator availability and more recently weather. These uncertain parameters present an even more compelling problem at the distribution level, as these networks are inherently unbalanced, and need to be represented as such for certain tasks. The work in this paper presents an information gap decision theory based three-phase optimal power flow. Assuming that the demand is uncertain, the aim is to provide optimal and robust tap setting and switch decisions over a 24-hour period,while ensuring that the network is operated safely, and that losses are kept within an acceptable range. The formulation is tested on a section of realistic low voltage distribution network with switches and tap changers present.

Worldwide, variable-speed wind turbine and solar photovoltaic generation are displacing conventional power plant in market schedules. Committing out-of-merit conventional units to redress system synchronous inertia or primary frequency response shortfalls incurs start-up and production costs, and may also engender additional greenhouse gas emissions and wind/solar curtailment. In order to ensure that future system frequency response requirements are met in a low carbon manner, new sources of frequency stability ancillary services will need to be incentivised or mandated via grid codes. Nonsynchronous devices (batteries, flywheels, variable-speed wind turbines), with appropriate control architectures, can provide a fast frequency response following a system disturbance, i.e. a temporary injection of active power, supplied faster than existing primary frequency response deployment times. Operational considerations relevant to transmission system operators when designing a fast frequency response ancillary service are presented, particularly if sourced from wind power emulated inertial response. It is shown that careful consideration regarding the design of fast frequency response characteristics is required in high wind power systems: the system frequency response behaviour may be degraded if a holistic approach to fast frequency response design is not taken. A method to characterise the system-wide (aggregate) emulated inertial response from wind power is presented, which can be integrated as a form of fast frequency response within unit commitment and economic dispatch. Endogenous incorporation in unit commitment and economic dispatch ensures that non-synchronous fast frequency response sources do not only supplement existing fossil fuel-based spinning reserve provision, but also reduce the need to commit synchronous generators for frequency control reasons. However, given the inherent energy recovery/payback experienced by variable-speed wind turbines providing emulated inertial response when operating below rated output, it is imperative to consider the impact of such negative power trajectories on system primary frequency response requirements.

The paper is concerned with the development and evaluation of control algorithms for the implementation of demand response strategies in a smart-grid enabled all-electric residential building. The dwelling is equipped with a 12 kW heat pump, a 0.8 m3 water storage tank, a 6 kW photovoltaic (PV) array, solar thermal collectors for domestic hot water heating and an electric vehicle. The building, located in Ireland, is fully instrumented. An EnergyPlus building simulation model of the dwelling was developed and calibrated using monitored data from the building. The developed model is used to assess the effectiveness of demand response strategies using different time-of-use electricity tariffs in conjunction with zone thermal control. A reduction in generation cost (−22.5%), electricity end-use expenditure (−4.9%) and carbon emission (−7.6%), were estimated when DR measures were implemented and compared with a baseline system. Furthermore, when the zone control features were enabled, the efficiency of the control improved significantly giving, an overall annual economic saving of 16.5% for the residential energy cost. The analysis also identified an annual reduction of consumer electricity consumption of up to 15.9%, lower carbon emissions of 27% and facilitated greater utilisation of electricity generated by grid-scale renewable resources, resulting in a reduction of generation costs for the utility of up to 45.3%.

Advances in communications technology, higher penetration rates of renewable energy and an evolution towards smarter electrical grids are enabling a greater role from demand side response (DSR) in maintaining power system security and reliability. The provision of primary operating reserve (POR) from domestic loads through a decentralised, system frequency based approach is discussed. By considering a range of system configurations (generation mix, system generation and load) and control strategies, this paper endeavours to answer critical questions concerning the large-scale roll out of decentralised DSR, including the following: what are the implications of DSR resource seasonal variability on system operation and performance following the loss of a large infeed/load? Do increased load coincidence and energy payback phenomena have the potential to significantly impact system frequency recovery? How do DSR controller hardware characteristics influence the provision and effectiveness of reserve delivery? What are the repercussions of a 'fit and forget' approach to decentralised control from flexible load on frequency stability as the technology penetration increases? Can DSR be directly substituted for conventional reserve sources while recognising its post-event recovery period? Residential customer behaviour, seasonal effects and the diversity of individual device characteristics are recognised in a detailed thermodynamic flexible load model which is integrated with a detailed power system model to perform the analysis.

In restructured power systems, generation and commercialization activities became market activities, while transmission and distribution activities continue as regulated monopolies. As a result, the adequacy of transmission network should be evaluated independent of generation system. After introducing the constrained fuzzy power flow (CFPF) as a suitable tool to quantify the adequacy of transmission network to satisfy 'reasonable demands for the transmission of electricity' (as stated, for instance, at European Directive 2009/72/EC), the aim is now showing how this approach can be used in conjunction with probabilistic criteria in security analysis. In classical security analysis models of power systems are considered the composite system (generation plus transmission). The state of system components is usually modeled with probabilities and loads (and generation) are modeled by crisp numbers, probability distributions or fuzzy numbers. In the case of CFPF the component’s failure of the transmission network have been investigated. In this framework, probabilistic methods are used for failures modeling of the transmission system components and possibility models are used to deal with 'reasonable demands'. The enhanced version of the CFPF model is applied to an illustrative case.

Buildings tend to not operate as intended, and a pronounced gap often exists between measured and predicted environmental and energy performance. Although the causes of this ‘performance gap’ are multi-faceted, issues surrounding data integration are key contributory factors. The distributed nature of the Architecture, Engineering and Construction (AEC) industry presents many challenges to the effective capture, integration and assessment of building performance data. Not all building data can be described semantically, nor is it feasible to create adapters between many different software tools. Similarly, not all building contextual data can easily be captured in a single product-centric model. This paper presents a new solution to the problem based upon a hybrid architecture that links data which is retained in its original format. The architecture links existing and efficient relational databases storing time-series data and semantically-described building contextual data. The main contribution of this work is an original RDF syntax structure and ontology to represent existing database schema information, and a new mechanism that automatically prepares data streams for processing by rule-based performance definitions. Two test cases evaluate the concept by 1) applying the hybrid architecture to building performance data from an actual building, and 2) evaluating the efficiency of the architecture against a purely RDF-based solution that also stores all of the time-series data in RDF for a virtual building. The hybrid architecture also avoids the duplication of time-series data and overcomes some of the differences found in database schemas and database platforms.

Recent demand for higher energy efficiency within the building sector has led to the use of Building Energy Performance Simulation (BEPS) tools. These powerful predictive tools enable investigation of environmental and energy performance for different design and retrofit design alternatives. However, integrating BEPS with Building Information Modelling (BIM) based building design tools still experiences limitations due to a lack of standardised methods of information exchange between these domains. As a result, this paper presents a Model View Definition (MVD) for advanced BEPS. In doing so this work enables a standardised exchange of data from BIM to BEPS tools, such as Modelica, using the Industry Foundation Classes (IFC) standard. The entire process becomes available through the open source software framework emerged from the IEA EBC Annex 60.

Thermal Electric Storage (TES) has emerged as a promising technology for enhancing the flexibility of the built environment to participate in active Demand Side Management (DSM). These devices allow the decoupling of intra-day scheduling of electric power demand from the time of thermal energy end-use. Therefore, if enabled with communication with the grid, these devices can facilitate load shifting and energy arbitrage. This study evaluates the energy arbitrage value of smart TES devices in residential buildings across Ireland. A Building-to-Grid (B2G) model has been developed which integrates the buildings thermal dynamics and end-use constraints with the power systems economic dispatch model. The thermal behavior of the houses and the TES space heater and hot water tank is modeled through linear state space models for three different mid-flat archetypes. The optimization results show the load shifting and arbitrage potential of TES and its impacts on wind curtailment considering various penetration levels of these devices.