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An emission rate-based carbon tax is applied to fossil-fueled generators with a demand response approach called Smart Grid resource allocation (SGRA). The former reduces the capacity factors (CFs) of base load serving fossil-fueled units, while the latter reduces the CFs of peak load serving units. The objective is to quantify the integration of the carbon tax and the SGRA approach on CO2 emissions and electricity prices in a multi-area power grid. We illustrate this using the Roy Billinton test system and the results show potential for significant reductions in fossil fuel-based generation and CO2 emissions. [ABSTRACT FROM AUTHOR]

The optimum inclination and orientation of fixed photovoltaic (PV) panels has long been defined in terms of maximizing the annual electricity yield per capacity installed according to the hemisphere and latitude where the PV system is located. Such optimum setup would thus also maximize the output per system cost, but it would not maximize the output per unit of available area, and it would not necessarily optimize the contribution of photovoltaic electricity vis-à-vis overall electricity demand patterns. This study seeks to draw the attention of policy-makers to the fact that incentivizing lower-than-optimum PV panel tilt angles can be an inexpensive strategy to substantially increase the renewable electricity yield in a given area. It also discusses how such strategy can be incorporated into an overall supply/demand grid management and renewable energy integration plan.

There is growing interest in zero-energy and low-energy buildings, which have a net energy consumption (on an annual basis) of almost zero. Because they can generate both electricity and thermal energy through the use of solar photovoltaic (PV) and solar thermal collectors, and with the help of reduced building thermal demand, low-energy buildings can not only make a significant contribution to energy conservation on an annual basis, but also reduce energy consumption and peak demand. This study focused on electricity consumption during the on-peak period in a low-energy residential solar building and considers the use of a building’s thermal mass and thermal storage to reduce electricity consumption in summer and winter by modulation of temperature setpoints for heat pump and indoor thermostats in summer and additional use of a solar heating loop in winter. Experiments were performed at a low-energy solar demonstration house that has solar collectors, hot water storage, a ground-coupled heat pump, and a thermal storage tank. It was assumed that the on-peak periods were from 2 pm to 5 pm on hot summer days and from 5 pm to 8 pm on cold winter days. To evaluate the potential for utilizing the building’s thermal storage capacity in space cooling and heating, the use of simple control strategies on three test days in summer and two test days in the early spring were compared in terms of net electricity consumption and peak demand, which also considered the electricity generation from solar PV modules on the roof of the house.

Cool roof coatings have a beneficial impact on reducing the heat load of a range of building types, resulting in reduced cooling energy loads. This study seeks to understand the extent to which cool roof coatings could be used as a residential demand side management (DSM) strategy for retrofitting existing housing in a constrained network area in tropical Australia where peak electrical demand is heavily influenced by residential cooling loads. In particular this study seeks to determine whether simulation software used for building regulation purposes can provide networks with the ‘impact certainty’ required by their DSM principles. The building simulation method is supported by a field experiment. Both numerical and experimental data confirm reductions in total consumption (kWh) and energy demand (kW). The nature of the regulated simulation software, combined with the diverse nature of residential buildings and their patterns of occupancy, however, mean that simulated results cannot be extrapolated to quantify benefits to a broader distribution network. The study suggests that building data gained from regulatory simulations could be a useful guide for potential impacts of widespread application of cool roof coatings in this region. The practical realization of these positive impacts, however, would require changes to the current business model for the evaluation of DSM strategies. The study provides seven key recommendations that encourage distribution networks to think beyond their infrastructure boundaries, recognising that the broader energy system also includes buildings, appliances and people.

Abstract Power demand management, particularly demand response controls, is the effort from the demand side to help the supply side management for helping maintain grid balance and also bring the economic benefits for building owners. Most of building demand management and demand response control strategies for commercial buildings only focus on a single building. However, as for the cluster-level buildings, which are sometimes involved in an account for electricity charge and also the main concern of smart grids, such conventional individual-level control strategies will not be effective. A game theory-based decentralized control strategy is therefore developed for cluster-level building demand management by simultaneously optimizing the indoor air temperature set-point and the operation of active thermal storage. Without gathering all the required information of buildings to a central optimization system, the cluster-level building demand management can be realized in a decentralized way. Case studies are conducted and results show that the proposed decentralized control strategy can increase the peak demand reduction, over two times than that when the demand management of buildings is conducted in an uncoordinated mode. Meanwhile, the performance of the proposed decentralized control strategy is closely approached to the results that are optimized in a centralized mode. [ABSTRACT FROM AUTHOR]

Highlights • Australia's largest appliance level consumption monitoring data set studied. • Actual contribution of air-conditioners to regional summer demand peaks analysed. • K-means clustering performed to categorize air-conditioner usage patterns. • Two scenarios considered for air-conditioner demand response at NSW State level. • Findings suggest possible NSW peak demand reductions of around 4–9%. Abstract Residential air-conditioner loads are known to be a key driver of summer demand peaks for some electricity industries. Such demand peaks occur infrequently but have potentially severe implications for network and peaking generation plant investment, and hence electricity prices for end users. Detailed assessments of air-conditioner peak demand have, however, been hampered by limited residential consumption data, and even when interval meter data is available, the complexities of other household loads and behavior. This study utilizes data from a significant appliance level consumption monitoring project in Sydney, Australia, to analyze the actual contribution of air-conditioners to regional summer demand peaks. K-means clustering is used to characterize air-conditioner load profiles across a large number of households at times of these overall demand peaks. These clustered air-conditioning load profiles, combined with a number of possible load control strategies, are then used to estimate possible peak load reductions from air-conditioner demand response across the Australian State of New South Wales. Our findings suggest that residential air-conditioning presents a significant demand response opportunity, approaching perhaps 9% of total peak demand in some circumstances. [ABSTRACT FROM AUTHOR]

In order to overcome the negative effects of climate change and ensure a global low-carbon future, decarbonizing the electricity sector has been recognized as an important focus area. Internationally, policymakers use average carbon intensity (in gCO2-e/kWh) in calculating greenhouse gas (GHG) emissions from the electricity system. However, average carbon intensity is a single rate and a fixed quantity; thus, it does not provide any information about the time-varying nature of carbon intensity. The focus of this paper is to show the usefulness of time-varying carbon intensity estimation, which can provide detailed insights into GHG emissions, and help in identifying potential emission cut opportunities from the electricity sector in order to lessen atmospheric pollution. Time-varying carbon intensity estimation (i) reveals temporal variability of carbon intensity, (ii) explores the interplay between generations and emissions, (iii) identifies peak carbon-intensive hours, and (iv) provides evidence for designing appropriate demand-side management strategies with respect to GHG emission reduction. [ABSTRACT FROM AUTHOR]

Electricity consumption in the Kingdom of Saudi Arabia (KSA) has grown at an annual rate of about 7% as a result of population and economic growth. The consumption of the residential sector accounts for over 50% of the total energy generation. Moreover, the energy consumption of air-conditioning (AC) systems has become 70% of residential buildings' total electricity consumption in the summer months, leading to a high peak electricity demand. This study investigates solutions that will tackle the problem of high energy demand associated with KSA's air-conditioning needs in residential buildings. To reduce the AC energy consumption in the residential sector, we propose the use of smart control in the thermostat settings. Smart control can be utilized by (i) scheduling and advance control of the operation of AC systems and (ii) remotely setting the thermostats appropriately by the utilities. In this study, we model typical residential buildings and, crucially, occupancy behavior based on behavioral data obtained through a survey. The potential impacts in terms of achievable electricity savings of different AC operation modes for residential houses of Riyadh city are presented. The results from our computer simulations show that the solutions intended to reduce energy consumption effectively, particularly in the advance mode of operation, resulted in a 30% to 40% increase in total annual energy savings. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The electricity use on Swedish roads using electric road systems (ERS) is analyzed. • ERS hourly electricity use coincides with present peaks in the electricity system. • An ERS could increase the electricity demand of the dimensioning load hour by 4%. • An ERS could reduce Swedish road-transportation CO 2 emissions by 19%. • Applying ERS to all Swedish main roads could electrify 50% of annual traffic. Abstract This study investigates the hourly electricity demand related to implementing an electric road system (ERS) on five Swedish roads with the highest traffic flows that connect the three largest cities in Sweden. The study also compares the energy demands and the CO 2 mitigation potentials of the ERS with the use of carbon-based fuels to obtain the same transportation work, and extrapolates the results to all Swedish European- and National- (E- and N) roads. The hourly electricity demand along the roads are derived by linking 12 available measurement points for hourly road traffic volumes with 12,553 measurement points for the average daily traffic flows along the roads. The results show that applying an ERS to the five Swedish roads with the highest traffic flows can reduce by ∼20% the levels of CO 2 emissions from the road transport sector, while increasing by less than 4% the hourly electricity demand on the peak dimensioning hour. Extending the ERS to all E- and N-roads would electrify almost half of the vehicle kilometers driven annually in Sweden, while increasing the load of the hourly peak electricity demand by only ∼10% on average. [ABSTRACT FROM AUTHOR]

Abstract This paper examines changes residential rooftop solar customers make to their demand and energy usage when they take service on a demand rate, and the impact those changes have on their bills and the utilities' cost of service. [ABSTRACT FROM AUTHOR]

Voltage unbalance and magnitude violations under normal operating conditions have become main power quality problems in many low voltage (LV) distribution networks. Maintaining the voltage level in an LV network within the standard limits is the main constraining factor in increasing the network hosting ability for rooftop photovoltaic (PV). This study presents a new effective method for voltage management in unbalanced distribution networks through the implementation of optimal residential demand response (DR) and on-load tap changers (OLTCs). The proposed method minimises the compensation costs of voltage management (cost of DR and network loss), while prioritises the consumer consumption preferences for minimising their comfort level violations. A modified particle swarm optimisation algorithm (MPSO) is utilised to identify the optimal switching combination of household appliances and OLTC tap positions for the network voltage management. The proposed method is comprehensively examined on a real three-phase four-wire Australian LV network with considerable unbalanced and distributed generations. Several scenarios are investigated for improving the network voltage magnitude and unbalance considering individual and coordinated operations of DR and OLTCs (three phase tap control and independent phase tap control). Simulation results show that the coordinated approach of DR and OLTC, especially, DR integrated with OLTC independent phase tap control effectively improves the network voltage and increases the PV hosting capacity. [ABSTRACT FROM AUTHOR]

Background: Daylight saving time (DST) has been actively used as a mechanism for energy conservation and reduction of greenhouse gas emissions. In the case of Argentina, the most recent experiences with DST occurred during the austral summer periods of 2007-2008 and 2008-2009, when the policy was finally abandoned. However, the benefits of DST and the size of the (potential) energy savings are still part of an ongoing discussion in a country where energy subsidies imply a heavy fiscal burden. Methodology: Using a difference-in-differences framework that exploits the quasi- experimental nature of the program implementation, we use hourly data for the 2005-2010 period at the province level and estimate the impact of DST on electricity consumption and on peak demand. Results: The application of DST increased total electricity consumption between 0.4% and 0.6%, but decreased aggregate national peak demand between 2.4% and 2.9%. In monetary terms, DST represented extra generation costs of 10.9 and 18 million USD during summers 2007-2008 and 2008-2009, respectively. Finally, the application of DST increased the emissions of air pollutants during those periods. Conclusion: The rationale for DST is questionable. The policy outcomes in terms of energy consumption and energy peak demand seem to go in opposite directions, at least in the latest experience in Argentina. A case-by-case study is the safest way of proceeding, and this paper is a piece of evidence that contributes to an open debate. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from electricity generation are generally assessed using a yearly average carbon intensity (in carbon dioxide equivalent emissions per unit of energy). This masks the variability of emissions associated with different forms of generation over different timescales. Variability is a characteristic of electricity systems with high levels of renewable generation, where fossil fuels are typically used to meet any shortfall in supply. In this paper we argue that quantification of the time variability of carbon intensity is necessary to understand the detailed patterns of carbon emissions in electricity systems, particularly as future systems are likely to increasingly rely on a mix of time-variable generation types such as wind, hydro and solar. We analysed the time-varying carbon intensity of New Zealand's electricity sector, which has approximately 80% renewable generation. In contrast to many other nations, we found that carbon intensity did not consistently follow daily peak demand, and was only weakly correlated with demand. This result, and the finding that carbon intensity has significant seasonal variation, stems from the dominance of hydro (albeit with limited storage capacity) in New Zealand's generation mix. Further investigation of the operating regimes of the fossil fuel generators, using time-varying analysis, indicates that New Zealand's electricity system is sub-optimal from a GHG emission perspective, with more coal generation than would seem to be required. Two policy measures, which also generalize to other countries, are proposed to address this issue: (i) the creation of an electricity capacity market – providing revenue for standby fossil fuel generation capacity without the need for continual generation; (ii) use of time-varying carbon intensity to inform demand-side measures and decisions about new renewable generation. [ABSTRACT FROM AUTHOR]

A real PV array combined with two storage solutions (B, battery, and H, hydrogen reservoir with electrolyzer-fuel cells) is modeled in two geolocations: Oxford, UK, and San Diego, California. All systems meet the same 1-year, real domestic demand. Systems are first configured as standalone (SA) and then as Grid-connected (GC), receiving 50% of the yearly-integrated demand. H and PV are dynamically sized as function of geolocation, battery size B M and H’s round-trip efficiency η H . For a reference system with battery capacity B M = 10  kW h and η H = 0.4 , the required H capacity in the SA case is ∼1230 kW h in Oxford and ∼750 kW h in San Diego (respectively, ∼830 kW h and ∼600 kW h in the GC case). Related array sizes are 93% and 51% of the reference 8 kW p system (51% and 28% for GC systems). A trade-off between PV size and battery capacity exists: the former grows significantly as the latter shrinks below 10 kW h, while is insensitive for B M rising above it. Such a capacity achieves timescales’ separation: B, costly and efficient, is mainly used for frequent transactions (daily periodicity or less); cheap, inefficient H for seasonal storage instead. With current PV and B costs, the SA reference system in San Diego can stay within 2 · 10 4 $ CapEx if H’s cost does not exceed ∼7 $/kW h; this figure increases to 15 $/kWh with Grid constantly/randomly supplying a half of yearly energy (6.5 $/kWh in Oxford, where no SA system is found below 2 · 10 4 $ CapEx). Rescaling San Diego’s array (further from its optimal configuration than Oxford’s) to the ratio between local, global horizontal irradiance (GHI) and Oxford GHI, yields in all cases a 11% reduction of size and corresponding cost, with the other model outputs unaffected. The location dependent results vary to different extents when extending the modeled timeframe to 18 years. In any case, the variability stays within ± 10 % of the reference year. [ABSTRACT FROM AUTHOR]

The direct load control (DLC) of populations of air conditioners (ACs) is a cost-effective demand-side strategy to manage peak demand and provide ancillary services to the electricity grid. Much research in recent years has focused on the formulation of mathematical models for the aggregate demand of such populations as a basis for DLC analysis and design. These models, however, are sensitive to ambient temperature, which is often assumed constant, and rely on the knowledge of thermal parameters that are typically difficult to obtain. This paper develops a new mathematical model to map the effect of ambient temperature to the aggregate demand response of a heterogeneous population of ACs. The significance of this new model lies in that it can be used to estimate the thermal parameters that characterize aggregate demand using available local weather and demand data, with no need for additional metering or direct engagement of grid users. While the estimation of these parameters is of independent interest in mapping distributed building energy performance over the grid, a key additional benefit is that these parameters fit existing models for DLC design, which provides the technical basis for practical model-based DLC of populations of ACs. [ABSTRACT FROM PUBLISHER]

The residential sector is key for electricity demand in many developed economies. Reducing electricity use in households is valuable for carbon mitigation and capacity adequacy and addressing fuel poverty. In many liberalised systems, a forward-capacity market is established to remunerate resources’ capacity value, with some allowing electricity use reduction to participate. This paper focuses on the Electricity Demand Reduction Pilot in the UK that trials a novel approach of incentivising electric efficiency via the Great Britain capacity market. Using a case study of social housing, it identifies barriers faced by the residential sector to utilise funding from the pilot. While opportunities exist for electricity use reduction in lighting, appliances and heating, financial incentives based on the impact on system peak demand are unlikely to be attractive and disadvantage insulation and efficient heating system. Limited budget for electric efficiency project and inflexible requirement of over 2-year payback of Electricity Demand Reduction (EDR) Pilot pose the challenge of funding projects, especially for small organisations, even if they can deliver capacity value to the electricity system. The obligation to deliver and verify committed peak savings and limited scope for payback present challenges and risks for projects to target potential opportunities within households. For communal electricity use, the minimum savings, cash flow and limited internal capabilities are constraints. Therefore, it is inadequate to rely on a forward-capacity market as a primary vehicle for incentivising electric efficiency investment in the residential sector, highlighting the importance of alternative provisions like supplier obligation and market transformation. [ABSTRACT FROM AUTHOR]

Australia has likely the world's highest residential photovoltaic (PV) system penetration. In this paper, the impact of distributed PV on peak demand at different distribution network zone substations (ZSs) is assessed by upscaling 15 min PV generation data from 270 distributed PV systems across Sydney, Australia, and comparing it with load data from 138 ZS serving the Sydney region. Gross load (load had there been no PV) was estimated, allowing the impact of current and higher PV penetrations on the value and time of peak at the different ZSs to be assessed. A probabilistic assessment of the impact of PV on ZSs is conducted, based on the availability of PV during the peak demand periods. To better understand the impact of PV on peak demand, K-means clustering is used to group ZSs based on PV generation during peak periods as the clustering features. Mapping of PV availability across percentage of peak times for all ZSs highlights the interannual variability of peak reductions and the potential impact of short-term load shifting. The impact of different penetration levels of distributed PV on the peak demand of the entire distribution network is also assessed by aggregating the ZS loads. [ABSTRACT FROM PUBLISHER]