• Energy Research

Aggregator-activated demand response (DR) is widely recognised as a viable means for increasing the flexibility of renewable and sustainable energy systems (RSESs) necessary to accommodate a high penetration of variable renewables. To this end, by acting as DR aggregators and offering energy trading capabilities to smaller customers, energy retailers unlock additional sources of demand-side flexibility to ensure the cost-optimal operation of RSESs. Accordingly, a growing body of literature has highlighted the ways in which non-cooperative game theory could be used to reduce the gaps between modelled and real-world results for aggregator-mediated DR schemes. This paper aims to contribute to the trends of giving a realistic grounding to research on distributed DR-integrated energy scheduling by using insights from non-cooperative game theory to determine: (1) the optimal trade-off between importing electricity and utilising DR capacity in grid-tied RSESs, (2) the impact of the price elasticity of DR supply of different customer classes – especially, new sources of electricity demand, such as e-mobility – on the system-level dispatch of DR resources, and (3) the financial implications of harnessing the flexibility potential of a large number of end-consumers across different sectors. Accordingly, the principal goal of the paper is to develop an operational planning optimisation model that can be directly applied to real-world aggregator-mediated, market-based demand-side flexibility provisioning domains. To this end, this paper presents the first DR elasticity-aware, non-cooperative game-theoretic DR scheduling model that: (1) yields the best compromise solution between imported power and dispatched DR resources from the utility's perspective, (2) characterises the utility-aggregator-customer interactions during the market-based DR trade process with several customer categories involved, and (3) disaggregates the total sectoral load on the system to individual end-consumers, which has potential implications for pre-feasibility and business case assessments. The application of the model to a conceptual micro-grid for the town of Ohakune, in New Zealand, demonstrates its effectiveness in reducing the daily system operational cost (over the critical peak hours) by ~66% and ~47% on a representative summer and winter day, respectively. Importantly, the paper provides statistically significant evidence supporting that activating the flexibility potential of small- to medium-scale end-consumers through specifically defined third-party aggregators in a market-based approach – that is aware of strategic interactions among instrumentally rational economic agents involved in the dispatch and delivery of DR resources – plays a significant role in the cost-optimal transition to 100%-renewable electricity generation systems within the smart grid paradigm.

IntroductionThe evaluation of the Victorian Healthy Homes Program (VHHP) will generate evidence about the efficacy and cost-effectiveness of home upgrades to improve thermal comfort, reduce energy use and produce health and economic benefits to vulnerable households in Victoria, Australia.Methods and analysisThe VHHP evaluation will use a staggered, parallel group clustered randomised controlled trial to test the home energy intervention in 1000 households. All households will receive the intervention either before (intervention group) or after (control group) winter (defined as 22 June to 21 September). The trial spans three winters with differing numbers of households in each cohort. The primary outcome is the mean difference in indoor average daily temperature between intervention and control households during the winter period. Secondary outcomes include household energy consumption and residential energy efficiency, self-reported respiratory symptoms, health-related quality of life, healthcare utilisation, absences from school/work and self-reported conditions within the home. Linear and logistic regression will be used to analyse the primary and secondary outcomes, controlling for clustering of households by area and the possible confounders of year and timing of intervention, to compare the treatment and control groups over the winter period. Economic evaluation will include a cost-effectiveness and cost-benefit analysis.Ethics and disseminationEthical approval was received from Victorian Department of Human Services Human Research Ethics Committee (reference number: 04/17), University of Technology Sydney Human Research Ethics Committee (reference number: ETH18-2273) and Australian Government Department of Veterans Affairs. Study results will be disseminated in a final report and peer-reviewed journals.Trial registration numberACTRN12618000160235.

Improving the efficiency and performance of the UK residential sector is now necessary for meeting future energy and climate change targets. Building performance evaluation and certification (BPEC) tools are vital for estimating and recommending cost effective improvements to building energy efficiency and lowering overall emissions. In the UK, building performance is estimated using the standard assessment procedure (SAP) for new dwellings and Reduced SAP (RdSAP) for existing dwellings. Using a systems based approach we show there are many opportunities for improving the effectiveness of BPEC tools. In particular, if the building stock is going to meet future energy and climate change targets the system driving building energy efficiency will need to become more efficient. In order to achieve this goal, building performance standards across Europe are compared highlighting the most effective strategies where they are found. It is shown that the large variance between estimated and actual energy performance from dwellings in the UK may be preventing the adoption of bottom-up energy efficiency measures. We show that despite popular belief, SAP and RdSAP do not estimate building energy efficiency but instead attempt to estimate the cost-effective performance of a building and thus create perverse incentives that may lead to additional CO2 emissions. In this regard, the SAP standard confounds cost-effectiveness, energy efficiency and environmental performance giving an inadequate estimate of all three policy objectives. Important contributions for improving measurement, analysis, synthesis and certification of building performance characteristics are offered.

With China’s commitment to peak its emissions by 2030, sectoral emissions are under the spotlight due to the rolling out of the national emission trading scheme (ETS). However, the current sector policies focus either on the production side or consumption while the majority of sectors along the transmission were overlooked. This research combines input–output modelling and network analysis to track the embodied carbon emissions among thirty sectors of thirty provinces in China. Based on the large-data resolution network, a two-step network reduction algorithm is used to extract the backbone of the network. In addition, network centrality metrics and community detection algorithms are used to assess each individual sector’s roles, and to reveal the carbon communities where sectors have intensive emission links. The research results suggest that the sectors with high out-degree, in-degree or betweenness can act as leverage points for carbon emissions mitigation. In addition to the electricity sector, which is included in the national ETS, the study also found that the metallurgy and construction sectors should be prioritized for emissions reduction from national and local levels. However, the hotpots are different across provinces and thus provincial specific targeted policies should be formed. Moreover, there are nineteen carbon communities in China with different features, which provides direction for provincial governments’ external collaboration for synergistic effects.

As global fuel reserves are depleted, alternative and more efficient forms of energy generation and delivery will be required. Combined heat and power with district heating (CHP-DH) provides an alternative energy production and delivery mechanism that is less resource intensive, more efficient and provides greater energy security than many popular alternatives. It will be shown that the economic viability of CHP-DH networks depends on several principles, namely (1) the optimisation of engineering and design principles; (2) organisational and regulatory frameworks; (3) financial and economic factors. It was found that in the long term DH is competitive with other energy supply and distribution technologies such as electricity and gas. However, in the short to medium term it is shown that economic risk, regulatory uncertainty and lock-in of existing technology are the most significant barriers to CHP-DH development. This research suggests that under the present regulatory and economic paradigm, the infrastructure required for DH networks remains financially prohibitive; the implementation of government policies are complicated and impose high transaction costs, while engineering solutions are frequently not implemented or economically optimised. If CHP-DH is going to play any part in meeting climate change targets then collaboration between public and private organisations will be required. It is clear from this analysis that strong local government involvement is therefore necessary for the co-ordination, leadership and infrastructural deployment of CHP-DH.

Abstract An energy hub is a multi-carrier energy system that is capable of coupling various energy networks. It increases the flexibility of energy management and creates opportunities to increase the efficiency and reliability of energy systems. When plug-in hybrid electric vehicles (PHEVs) are incorporated into the energy hub, batteries can act as an aggregated storage system, increasing the potential integration of variable renewable energy sources (RES) into power system networks. This paper presents a new model for the optimal operation of an energy hub that includes RES, PHEVs, fuel cell vehicles, a fuel cell, an electrolyzer, a hydrogen tank, a boiler, an inverter, a rectifier, and a heat storage system. A novel model is developed to estimate the uncertainty associated with the power consumption of PHEVs during trips using information gap decision theory (IGDT) under risk-averse and risk-seeking strategies. Simulation results demonstrate that the proposed method maximizes the objective function under the risk-neutral and risk-averse strategies, while minimizing the objective function under the risk-seeking strategy. Results from the modeling show that considering the uncertainty associated with the power consumption of PHEVs using IGDT enables the energy hub operator to make appropriate decisions when optimizing the operation of the energy hub against possible changes in power consumption of PHEVs.

Abstract In this paper, panel methods are applied in new and innovative ways to predict daily mean internal temperature demand across a heterogeneous domestic building stock over time. This research not only exploits a rich new dataset but presents new methodological insights and offers important linkages for connecting bottom-up building stock models to human behaviour. It represents the first time a panel model has been used to estimate the dynamics of internal temperature demand from the natural daily fluctuations of external temperature combined with important behavioural, socio-demographic and building efficiency variables. The model is able to predict internal temperatures across a heterogeneous building stock to within ∼0.71 °C at 95% confidence and explain 45% of the variance of internal temperature between dwellings. The model confirms hypothesis from sociology and psychology that habitual behaviours are important drivers of home energy consumption. In addition, the model offers the possibility to quantify take-back (direct rebound effect) owing to increased internal temperatures from the installation of energy efficiency measures. The presence of thermostats or thermostatic radiator valves (TRVs) are shown to reduce average internal temperatures, however, the use of an automatic timer is shown to be statistically insignificant. The number of occupants, household income and occupant age are all important factors that explain a quantifiable increase in internal temperature demand. Households with children or retired occupants are shown to have higher average internal temperatures than households who do not. As expected, building typology, building age, roof insulation thickness, wall U -value and the proportion of double glazing all have positive and statistically significant effects on daily mean internal temperature. In summary, the model can be either used to make statistical inferences about the importance of different factors for explaining internal temperatures or as a predictive tool. However, a key contribution of this research is the possibility to use this model to calibrate existing building stock for behaviour and socio-demographic effects leading to improved estimations of domestic energy demand.