Grid Electricity Demand Research Articles

Electrification of residential heating, cooling and hot water: Load smoothing using onsite photovoltaics, heat pump and thermal batteries

Due to the pressing timeframe to meet emissions reduction targets, electrification of heat is the fastest approach for decarbonising buildings. However, the high power demand of electric heating and cooling devices poses a significant challenge to the electricity supply chain when they are deployed on mass. Adding rooftop solar photovoltaic systems does not necessarily eliminate this problem. Cheap and safe thermal storage offers a potentially viable solution allowing electric HVAC & hot water systems to be deployed with minimal impact on the grid. We propose such a multi-function thermal system combining solar photovoltaics, heat pump, and thermal storage for supplying residential heating, cooling and hot water demand. We investigate the effectiveness of this system in terms of electricity demand management and solar utilisation in different geographical locations. The results illustrate that with effective control, such a system can reduce a building’s annual grid-electricity demand by about 50% to 80%, and increase its solar self-consumption to around 60%. The temporal load profile is also significantly reduced and flattened, allowing high level of distributed PV penetration without negative impacts on the grid. We show that these benefits can be earned at a reasonable cost to consumers helping them to reap a positive rate of return compared to alternative options for different climatic and regional conditions. The proposed thermal storage system is demonstrated as a cost-effective and safe distributed energy solution to ease the stress exerted on the grid from excess PV power as well as heating and cooling related peak demand.

Residential demand-side management using integrated solar-powered heat pump and thermal storage

Due to the mismatch between rooftop solar energy generation and residential electrical demand, considerable solar energy is exported into electricity networks causing a range of issues such as voltage instability and connection overload. To address these problems, we propose and analyse a residential hot water, heating and cooling system, which features a heat pump combined with thermal energy storage to align peak thermal loads with output from a rooftop solar system. This work quantifies the impacts of thermal storage on residential space-conditioning peak load reduction. Annual hourly thermal and domestic hot water loads were determined for a representative Australian house, located in Brisbane, using building energy simulation software and verified using measured data. Combined with the measured sub-metered electrical loads of other electrical appliances, this data was used to simulate the solar system export, heat pump demand, and thermal storage system performance. Results show that by combining a 5-kW solar system, the proposed system can reduce annual grid-electricity demand by approximately 76% compared with a conventional non-thermal storage system. Peak electrical load was also observed to undergo a temporal shift and reduce by approximately 45%. Furthermore, the solar fraction for air-conditioning and domestic hot water loads reached 84%, while solar self-consumption increased to about 56%. This study demonstrates that the proposed system is an effective means of managing electricity demand, shifting peak load and improving solar utilization, thus relieving stress on electricity networks from high penetration of solar photovoltaics.

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.

Greenhouse Gas Emissions of Stationary Battery Installations in Two Renewable Energy Projects

The goal to decrease greenhouse gas (GHG) emissions is spurring interest in renewable energy systems from time-varying sources (e.g., photovoltaics, wind) and these can require batteries to help load balancing. However, the batteries themselves add additional GHG emissions to the electricity system in all its life cycle phases. This article begins by investigating the GHG emissions for the manufacturing of two stationary lithium-ion batteries, comparing production in Europe, US and China. Next, we analyze how the installation and operation of these batteries change the GHG emissions of the electricity supply in two pilot sites. Life cycle assessment is used for GHG emissions calculation. The regional comparison on GHG emissions of battery manufacturing shows that primary aluminum, cathode paste and battery cell production are the principal components of the GHG emissions of battery manufacturing. Regional variations are linked mainly to high grid electricity demand and regional changes in the electricity mixes, resulting in base values of 77 kg CO2-eq/kWh to 153 kg CO2-eq/kWh battery capacity. The assessment of two pilot sites shows that the implementation of batteries can lead to GHG emission savings of up to 77%, if their operation enables an increase in renewable energy sources in the electricity system.

A simulation methodology on grid displaced photovoltaic (PV) prospective for existing terrace house supply in Malaysian peninsular.

Accounting for the government’s intention towards sustainable energy development and the deployment of Malaysia’s first smart grid system, inclusive participation of residential units will transform their role from consumers to prosumers, enabling them to become “positive nodes” of the smart grid system. This paper evaluates the simulation output of a grid displaced electricity demand of a significant terrace-based housing cohort in Malaysia. Utilising Integrated Environmental Solution Virtual Environment (IESVE) as the simulation tool, solar panel retrofitted to the existing terrace houses in urban centres across peninsular Malaysia is investigated in order to identify its potential to displace current grid electricity demand, consequently shaving peak demand and reduced household’s ‘s electricity bill.

Achieving energy resilience through smart storage of solar electricity at dwelling and community level

This paper empirically evaluates the extent of energy resilience achieved in a socially-deprived community in Oxford, through deployment of solar photovoltaic (PV) systems and smart batteries (internet enabled and controllable) across a cluster of 82 dwellings (households). The methodological approach comprised dwelling and household surveys, along with high frequency monitoring of household electricity consumption, solar PV generation, battery charge and discharge data. In the monitored households, average daily electricity consumption was found to be positively related with dwelling size, number of occupants and number of appliances used. Although 117 MWh of PV electricity was generated within a year across 74 dwellings, peak generation did not match peak consumption, demonstrating the need for battery storage. Home batteries were found to increase self-consumption of PV electricity and offset grid demand through discharge of stored PV electricity marginally at an average of 6%, depending on the size of the PV system, surplus PV electricity available and size of the battery. Aggregating solar generation and storage at a community level showed that peak grid electricity demand between 17:00 and 19:00 was reduced by 8% through the use of smart batteries across 74 dwellings. In future, a local energy sharing scheme could be developed, wherein not all dwellings would need to have solar PV systems, but rather have internet enabled batteries that could be monitored and controlled virtually.

A multi-scenario life cycle impact comparison of operational energy supply techniques for an office building in Thailand

This study provides a life cycle assessment (LCA) of a sustainably designed office building to be built in Thailand. The building has a gross floor area of 6300 m2 and a lifetime of 50 years. An analysis of four different energy supply scenarios was performed to evaluate methods for reducing grid electricity demand with the goal of optimizing renewable energy usage and minimizing environmental impacts. The scenarios included: (1) the conventional, grid-dependent building, followed by (2) the addition of a rooftop photovoltaic (PV) system, (3) a PV system with lithium-ion battery storage, and (4) a PV system with an ice storage system. Scenarios 3 and 4 were included in this study as 16% of the electricity from PV was overproduced during the weekends when the building was unoccupied. The results show that scenarios 2, 3, and 4 reduced operational grid consumption by 33%, 37.8%, and 37.9% but increased metal depletion potential by 23.9%, 34.4%, and 29.0%, respectively. Ice storage led to the greatest reduction in lifetime environmental impacts. Efficient production and utilization of renewable energy in buildings is vital to reducing nonrenewable fuel dependence; however, it is necessary to minimize metal depletion in the implementation of such technologies.

G<sup>2</sup>EDPS's First Module & Its First Extension Modules

100% renewable worldwide power grid (Global Grid) system needs a Global Grid Electricity Demand Prediction System (G²EDPS) with very short, short, medium and long term forecasting consoles. This paper presents the 1^st core module and its 10 extension modules in the long term prediction console. A type 1 Mamdani like Fuzzy Inference System (FIS) with 7 triangle membership functions and 49 rules is designed for 2 input and 1 output variables for a 100 year forecasting period. The maximum absolute percentage errors (MAP), the mean absolute percentage errors (MAPE), and the Symmetric MAPE (SMAPE) of the best core module and its extension modules are respectively 0, 24; 0, 08; 0, 05 and 0, 22; 0, 07; 0, 05.

Grid electricity for Fiji islands: Future supply options and assessment of demand trends

Electricity is a secondary energy source and one of the main drivers for economic development of a nation. Long-term planning for electricity demand is essential for strategic expansion of supply options which would require significant investment in terms of human resources and capital. This paper is focused on the past trends in annual grid-electricity demand for Fiji, from which forecast is done using statistically significant linear regression models. The regression models reveal that domestic grid-electricity demand variance is explained by population, GDP and electricity price. However, for non-domestic demand, the variance is explained by changes in population and GDP with electricity tariffs playing a small role. The absolute deviation of forecasts for total demand from 5 different regression models ranges from 1.2 to 32%. For domestic demand it ranges from 3.0 to 5.0% while non-domestic deviation ranges from 1.7 to 19%. Analytic hierarchy process was employed to choose the best model for demand forecast which then led the discussion on future supply options for grid-electricity expansion in Fiji. Biomass power plants, hydro and GCPV are seen to be the most promising supply.