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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]

This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands.

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.

With the rapid development of the civil aviation industry, the number of flights has increased rapidly. However, the availability of flight slot resources remains limited, and how to allocate flight slot resources effectively has been a hot research topic in recent years. A comprehensive flight slot optimization method can significantly enhance the rationality of the allocation results. The effective allocation of flight slot is the key to improving the operational efficiency of the multi-airport system. We will optimize the flight schedule of the entire multi-airport system considering the fairness of each airport in it. The optimization results will provide an important reference for the reasonable allocation of flight slot within the multi-airport system. Based on the operation characteristics of the multi-airport system, we have established a multi-objective flight slot allocation optimization model. In this model, we set the airport capacity limit, shared waypoint capacity limit and aircraft turnaround time limit as the constraints. The optimization goal of the model is to minimize total flight schedule displacement and the maximum deviation of fairness from the absolute fairness. Gurobi solver is used to solve the model. We have innovatively incorporated the rolling capacity constraint method into our model to ensure more accurate flight slot allocation results. The Beijing-Tianjin-Hebei regional multi-airport system is selected as an example to verify the above model, and the flight slot optimization results have successfully met the fairness goal. The comparative analysis has demonstrated that the rolling capacity constraint method significantly improves the accuracy of solution results, leading to more stable flight slot allocation. The results also prove that the flight slot allocation method of multi-airport system based on absolute fairness of peak demand can improve the fairness of the allocation results. To achieve a higher level of fairness, we have found that the peak-demand based fairness method requires a smaller slot displacement compared to the non-peak demand-based method. Through the optimization of flight slot of the multi-airport system, the coordination between airports can be significantly improved. It can provide a new solution for the efficient operation of the multi-airport system.

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]

This paper examines a successful residential peak electricity demand reduction project which employed a multi-disciplinary approach. The purpose of this study was to examine the critical success factors necessary for reducing peak demand and total energy consumption in a small Australian island community. This case study research is based on qualitative data obtained from semi-structured, in-depth interviews with residents from 22 households. It is proposed that the results of the examined project are transferable to other communities if the utility is able to develop the necessary trust, access, influence, and partnership with residential consumers required to create the environment for electricity demand reduction success. Findings from this research highlight the potential approach for future policymaking aimed at reducing peak electricity demand and total energy consumption in multiple communities, thus helping achieve government low carbon targets while reducing infrastructure spending. [ABSTRACT FROM AUTHOR]

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand. [ABSTRACT FROM AUTHOR]

Time of Use (TOU) pricing is a cost-reflective electricity pricing scheme; it has proven to be an effective approach for reducing peak electricity demand in the residential sector around the world, especially in developed countries. The implementation of TOU pricing in low and lower-middle income economies is less appealing than in other settings. This is mainly because a traditional TOU pricing scheme may increase the cost of electricity for low income consumers. The lack of a suitable TOU pricing strategy for these countries results in high peak demand, poor utilization of network infrastructure and, consequently, higher electricity prices than necessary. The purpose of this study is to analyse and propose a TOU pricing scheme for the residential sector that will be suitable for countries with a high percentage of low income household consumers. In this study, Bangladesh will be used as an exemplar of a lower-to-middle income developing country. In Bangladesh, the residential sector is responsible for half the country's total electricity consumption, and constitutes an even greater proportion of the peak demand. Residential consumers currently pay inclining block usage rates that provide no financial incentive for them to shift their electricity usage from peak to non-peak periods. The proposed TOU pricing scheme is a combination of the traditional TOU and inclining block usage pricing schemes, based on a realistic load shifting capacity that is applicable to Bangladesh, and to other similar developing countries. Analysis of this pricing system for different income levels of residential consumers shows that the proposed scheme effectively reduces the peak demand, while ensuring minimum impact on consumer monthly energy bills and comfort levels. [ABSTRACT FROM AUTHOR]