Electricity Demand Data Research Articles

Electric Two-Wheeler Vehicle Integration into Rural Off-Grid Photovoltaic System in Kenya

In both rural and urban areas, two-wheeler vehicles are the most common means of transportation, contributing to local air pollution and greenhouse gas emissions (GHG). Transitioning to electric two-wheeler vehicles can help reduce GHG emissions while also increasing the socioeconomic status of people in rural Kenya. Renewable energy systems can play a significant role in charging electric two-wheeled vehicles, resulting in lower carbon emissions and increased renewable energy penetration in rural Kenya. As a result, using the Conventional and Renewable Energy Optimization (CARNOT) Toolbox in the MATLAB/Simulink environment, this paper focuses on integrating and modeling electric two-wheeled vehicles (e-bikes) into an off-grid photovoltaic Water-Energy Hub located in the Lake Victoria Region of Western Kenya. Electricity demand data obtained from the Water-Energy Hub was investigated and analyzed. Potential solar energy surplus was identified and the surplus was used to incorporate the electric two-wheeler vehicles. The energy consumption of the electric two-wheeler vehicles was also measured in the field based on the rider’s driving behavior. The modeling results revealed an annual power consumption of 27,267 kWh, a photovoltaic (PV) electricity production of 37,785 kWh, and an electricity deficit of 370 kWh. The annual results show that PV generation exceeds power consumption, implying that there should be no electricity deficit. The results, however, do not represent the results in hourly resolution, ignoring the impact of weather fluctuation on PV production. As a result, in order to comprehend the electricity deficit, hourly resolution results are shown. A load optimization method was designed to efficiently integrate the electric 2-wheeler vehicle into the Water-Energy Hub in order to alleviate the electricity deficit. The yearly electricity deficit was decreased to 1 kWh and the annual electricity consumption was raised by 11% (i.e., 30,767 kWh), which is enough to charge four more electric two-wheeler batteries daily using the load optimization technique.

Aged Care Energy Use and Peak Demand Change in the COVID-19 Year: Empirical Evidence from Australia

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC’s yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use.

Smart Charging of Electric Vehicles: An Innovative Business Model for Utility Firms

Problem definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, after an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Academic/practical relevance: Utility firms are already investing in charging stations, and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers at the charging station. Results: We devise an intuitive and practically implementable policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the United States, we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers’ inconvenience cost of delays. We also find that most of the savings from implementing smart charging can be achieved during peak-demand days, highlighting the effectiveness of smart charging.

A comparative analysis of patterns of electricity use and flexibility potential of domestic and non-domestic building archetypes through data mining techniques

The large-scale deployment of smart meters has led to significant amount of electricity demand data available, driving it into the realm of Big Data. It is a major challenge to exploit this Big Data in order to characterise electricity use patterns and to support demand response policies. In this paper, we perform a featured-based cluster analysis on nine building archetypes (hospitals, schools, offices, hotels, flats, houses etc.) to identify electricity use patterns. Then, four metrics are developed, which are entropy, load curviness, peak intensity and index of hourly ramp rates, to measure these archetypes’ suitability to be involved in demand response schemes. A significant difference in electricity use patterns between the archetypes is found, as well as among the seasons and days of the week. We present a number of metrics for each archetype to establish which type of archetype should be prioritised for demand response programmes in terms of peak management, ramp rates as well as demand flexibility. A key finding of our study is that households offer more demand flexibility than the non-domestic sector and should therefore be incentivized to participate in dynamic electricity tariffs.

Optimal sizing of distributed energy resources for planning 100% renewable electric power systems

More utilities, energy providers, and governments are considering the transition to 100% renewable or carbon-free generation to satisfy electricity demand. This transition requires consideration of numerous factors including cost, resource adequacy, and geographical location, among others. Therefore, models that can explore the optimality of tradeoffs between multiple factors are crucial for planning this transition. An optimization problem formulation is proposed to analyze the amount of renewable generation and energy storage required to balance 100% of a utility's electricity demand on an hourly timescale over multiple years, while minimizing a desired cost. This formulation accounts for geographical location and accommodates regional energy trading, and it enables analysis of important metrics for planning, such as firm capacity, capacity factors, land area requirements, and amount of curtailed generation. This optimization-based approach is used to explore case studies in New Mexico, which is an area with significant potential for solar and wind generation in the United States. Considering multiple years of historical meteorological data and electricity demand data, results show that the amount of renewable generation required is an order of magnitude larger than the average demand, and that most of the generation is curtailed, which motivates a regional energy trading approach.

Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data

It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge.

The Impact of COVID-19 on Electricity Demand and Generation in Japan

This research considered changes in monthly electricity generation and demand in Japan during the COVID-19 pandemic. Observed network electricity demand and generation type for the January–June 2020 period were compared to forecast values (using a triple exponential smoothing method) based on trends established from 2016 to 2019. Regional level electricity demand data showed little variation from expected trends for domestic energy users, but lower than expected business and industrial network demand, particularly in the 50–2000 kW cohort. Electricity demand was most likely to deviate from existing trends in May 2020, which is in-line with the voluntary lockdown activities. These results are consistent with observed patterns from other international studies into the impact of COVID-19 on electricity demand. Generation was found to be reduced in May and June of 2020, without significant impacts to the generation makeup, largely due to Japan’s positioning within a broader energy transition context. These findings validate previous studies and add to the broader discussions on drivers and the rationale for electricity demand behaviors between user scales. Previous studies examined the electricity demand reductions of full and partial lockdowns. This analysis adds to this discourse by documenting the impacts of a voluntary lockdown.

The cost and emissions advantages of incorporating anchor loads into solar mini-grids in India

Renewables-based mini-grids have the potential to improve electricity access with lower emissions and better reliability than national grids. However, these systems have a challenging cost to revenue ratio, hindering their implementation. Combining residential loads with an anchor load, a relatively large non-domestic user, can help to improve mini-grid economics. Using measured electricity demand data from India and energy modelling, we assess the cost and emissions advantages of integrating health clinics as anchor loads within domestic solar mini-grids. For comparison, we also assess the ability of the national grid to meet our demand scenarios using monitored grid data. We apply a scenario-based approach, using separate domestic and anchor load demand profiles, and both in combination; we test meeting two levels of energy demand, 95% and 100%; and compare systems using PV and batteries, diesel, and hybrid generation. We find that the national grid has poor availability, at just over 50% at the most comparable monitoring site; and that it would meet a lower fraction of energy demand for our anchor load scenarios than the domestic only ones. For the off-grid systems, we find substantial cost and emissions reductions with anchor loads relative to demand scenarios without anchor loads. At 95% of demand met, we find PV and battery systems are 14-22% cheaper than diesel-only systems, with 10 times lower carbon intensity. Our findings illustrate the role off-grid systems can play in the provision of reliable low-carbon electricity and highlight the advantages of incorporating anchor loads like health centres into such systems.

Quantifying the trade-off between percentage of renewable supply and affordability in Pacific island countries: Case study of Samoa

Pacific island countries are particularly susceptible to sea level rise as a result of climate change and also face high costs and energy security issues from imported fossil fuels. As a result many Pacific island countries are perusing 100% renewable energy targets. Due to challenges with variable renewable energy sources, detailed studies that balance renewable energy supply and demand at a fine temporal scale have been carried out for many countries to inform policy directions. Despite having adopted renewable energy targets, most Pacific island countries have not been subject to the same level of detailed analysis in the academic literature. In addition, the results from other countries are not directly applicable given the particular local resources, climatic conditions and economic situation of Pacific island countries. In this paper, focusing on the case of Samoa, we use an approach based on historical electricity demand and generation time-series data to investigate future scenarios that achieve very high percentages of renewable electricity. The results show that scenarios of high proportions (above 90%) of renewable energy generation coupled with storage (28%–37% of direct solar, 17%–30% of stored solar and 25%–40% of hydro) are economically viable (Net Present Value >0) but, there is a significant trade-off between percentage of renewable supply and affordability. These results have important implications for energy policy directions for Samoa and are directly applicable to many other countries in the Pacific.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Due to the impact of occupants’ activities in buildings, the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term, which show seasonal variation and hourly variation, respectively. This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time. In order to solve this problem, this paper proposes two approaches for fitting and predicting the electricity demand of office buildings. The first proposed approach splits the electricity demand data into fixed time periods, containing working hours and non-working hours, to reduce the impact of occupants’ activities. After finding the most sensitive weather variable to non-working hour electricity demand, the building baseload and occupant activities can be predicted separately. The second proposed approach uses the artificial neural network (ANN) and fuzzy logic techniques to fit the building baseload, peak load, and occupancy rate with multi-variables of weather variables. In this approach, the power demand data is split into a narrower time range as no-occupancy hours, full-occupancy hours, and fuzzy hours between them, in which the occupancy rate is varying depending on the time and weather variables. The proposed approaches are verified by the real data from the University of Glasgow as a case study. The simulation results show that, compared with the traditional ANN method, both proposed approaches have less root-mean-square-error (RMSE) in predicting electricity demand. In addition, the proposed working and non-working hour based regression approach reduces the average RMSE by 35%, while the ANN with fuzzy hours based approach reduces the average RMSE by 42%, comparing with the traditional power demand prediction method. In addition, the second proposed approach can provide more information for building energy management, including the predicted baseload, peak load, and occupancy rate, without requiring additional building parameters.

Disruption, Disaster and Transition: Analysis of Electricity Usage in Japan from 2005 to 2016

This research analyzes the changes to electricity generation and demand in Japan following both the Global Financial Crisis and Disaster of March 2011. Monthly electricity demand and generation data for all regions of Japan from April 2005 to March 2016 were reviewed to identify differences in disruption-response between different types of electricity users. We apply inferential statistics to identify underlying trends, which we find are dominated by differences in user scale response. Higher capacity users reduced demand in response to the Global Financial Crisis, whereas smaller domestic scale users reduced electricity demand after the Disaster. Analysis reveals that regions within the 50Hz grid that were directly impacted by the Fukushima event and resulting load restrictions showed a statistically significant sustained reduction in monthly electricity demand post-disaster. However, Kansai and Shikoku, regions that are both outside the area directly impacted by the Fukushima event, also showed the same sorts of sustained significant reductions. By considering two disruptions to the same sociotechnical system we can draw conclusions that add to the discourse of electricity use behaviors, which informs both disaster response planning and policy for the broader issues of electricity demand reduction for climate stabilisation. In particular, the results highlight the importance of tailoring engagement and intervention actions to the preferences and needs of different types of electricity users.

Short-term impact of the COVID-19 lockdown on the energy and economic performance of photovoltaics in the Spanish electricity sector

The present work investigates how the COVID-19 related lockdown imposed in Spain in between March and June 2020 affected the national electricity sector and the share of photovoltaics in the energy mix. The analysis is conducted by comparing actual electricity demand, generation and price data with forecasts based on their pre-lockdown trends. The results show that the lockdown decreased the electricity demand by 11%, and affected even more severely the price of electricity, causing a total loss for the electricity sector of 6.1 million € per day. These losses were unevenly distributed among the different power technologies of the energy mix. The market share of photovoltaics raised by almost 1% because of the lockdown-related demand drop, even if it performed at capacity factors lower than expected. Overall, because of the lockdown and of the recently installed capacity, photovoltaic provided, for the first time, more than 9% of the national electricity consumed in June 2020, more than twice the maximum share achieved in the previous years.

Renewable energy analysis in indigenous communities using bottom-up demand prediction

This paper provides a methodology for the holistic analysis of hybrid renewable energy systems in rural communities. Electric demand is an important component for modeling and analysis of renewable energy systems. Typically, electric demand data is not available due to the internal privacy policies of utility providers. Therefore, this study proposes the use of bottom-up approaches for the development of the electric demand profile, considering the general homogeneity of residential and commercial buildings in rural communities. As a test case, this study develops the electric demand profile and investigates the technical and environmental feasibility of a hybrid renewable energy system for the New Town community on the Fort Berthold Indian Reservation (FBIR) in North Dakota. This study conducts the hybrid renewable energy system’s analysis by developing scripts in the LK scripting language and integrating System Advisor Model software’s open-source modules for modeling of renewable energy systems. The results for the validation testbed of this study show that hybrid renewable resources have higher ratios of energy used for self-consumption to the total energy generated compared to stand-alone wind and PV farms.

Wind and Solar Resource Droughts in California Highlight the Benefits of Long-Term Storage and Integration with the Western Interconnect.

As reliance on wind and solar power for electricity generation increases, so does the importance of understanding how variability in these resources affects the feasible, cost-effective ways of supplying energy services. We use hourly weather data over multiple decades and historical electricity demand data to analyze the gaps between wind and solar supply and electricity demand for California (CA) and the Western Interconnect (WECC). We quantify the occurrence of resource droughts when the daily power from each resource was less than half of the 39-year daily mean for that day of the year. Averaged over 39 years, CA experienced 6.6 days of solar and 48 days of wind drought per year, compared to 0.41 and 19 for WECC. Using a macro-scale electricity model, we evaluate the potential for both long-term storage and more geographically diverse generation resources to minimize system costs. For wind-solar-battery electricity systems, meeting California demand with WECC generation resources reduces the cost by 9% compared to constraining resources entirely to California. Adding long-duration storage lowers system costs by 21% when treating California as an island. This data-driven analysis quantifies rare weather-related events and provides an understanding that can be used to inform stakeholders in future electricity systems.

PVAnalytX: A MATLAB toolkit for techno-economic analysis and performance evaluation of rooftop PV systems

The adaptation of rooftop solar PV systems has increased significantly in recent years. Batteries have become an essential part of the rooftop solar PV systems, which ensures maximum utilisation of solar energy. With a view to present a clear insight to benefits of a rooftop solar PV system, considering both the technical and economic benefits, a MATLAB based PVAnalytX software toolkit has been presented in this paper. The end users can evaluate both the technical and economic benefits of a rooftop solar PV system considering solar generation and electricity demand variability, as well the battery performance degradation over the course of its usage. In addition, the mechanism to generate forecasted solar power generation and electricity demand data is also included as a feature in the proposed software toolkit. Furthermore, the toolkit considers actual electricity imports and export tariffs that allow the end consumers to know their financial expenditures for electricity consumption and income generated through the export of access rooftop PV energy to the grid. A thorough validation of the proposed toolkit has been presented through two case studies using actual dataset of rooftop PV systems located at CBD region of Sydney, Australia.

On the Improvement of representative demand curves via a hierarchical agglomerative clustering for power transmission network investment

This paper introduces an optimal clustering-based strategy to gain representative demand curves from hourly demand data that allow determining the power transmission network investment by solving the transmission expansion planning (TEP) problem. The proposed approach also provides a high-dimensionality data optimal reduction for the representative demand curves that feed the TEP problem. The key idea behind this strategy is to extract demand patterns from the electric power system demand data through the implementation of a hierarchical agglomerative clustering algorithm (HACA) based on the Elbow’s rule and a linkage criterion, such as Ward’s variance. Then, a 24-h demand pattern is provided by following three different grouping strategies: seasonal, monthly, and weekly. As a second stage, this strategy includes the TEP formulation together with the transmission losses’ linearised model aiming to test the representative demand curves achieved by HACA. To illustrate the efficiency, application, and superior functionality of the proposal, this is implemented over the IEEE 118-node network under several case studies. To determine the most appropriate approach, the results are compared with the well-known K-means method.

Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting

The current COVID-19 pandemic and the preventive measures taken to contain the spread of the disease have drastically changed the patterns of our behavior. The pandemic and movement restrictions have significant influences on the behavior of the environment and energy profiles. In 2020, the reliability of the power system became critical under lockdown conditions and the chaining in the electrical consumption behavior. The COVID-19 pandemic will have a long-term effect on the patterns of our behavior. Unlike previous studies that covered only the start of the pandemic period, this paper aimed to examine and analyze electrical demand data over a longer period of time with five years of collected data up until November 2020. In this paper, the demand analysis based on the time series decomposition process is developed through the elimination of the impact of times series correlation, trends, and seasonality on the analysis. This aims to present and only show the pandemic’s impacts on the grid demand. The long-term analysis indicates stress on the grid (half-hourly and daily peaks, baseline demand and demand forecast error) and the effect of the COVID-19 pandemic on the power grid is not a simple reduction in electricity demand. In order to minimize the impact of the pandemic on the performance of the forecasting model, a rolling stochastic Auto Regressive Integrated Moving Average with Exogenous (ARIMAX) model is developed in this paper. The proposed forecast model aims to improve the forecast performance by capturing the non-smooth demand nature through creating a number of future demand scenarios based on a probabilistic model. The proposed forecast model outperformed the benchmark forecast model ARIMAX and Artificial Neural Network (ANN) and reduced the forecast error by up to 23.7%.

Modeling Energy Communities with Collective Photovoltaic Self-Consumption: Synergies between a Small City and a Winery in Portugal

The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.

Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets

With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physics-based models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios.

The Economics of the Site C Hydroelectric Project in British Columbia

The Site C hydroelectric project is an 1100-Megawatt hydroelectric facility under development on the Peace River in British Columbia. Construction costs for the project are currently estimated to be $10.7 billion. The project has been criticized for its high cost and the impacts it will have on the river system, including the flooding of land to establish the reservoir. Project proponents argue that Site C will provide low-carbon electricity that will help BC and Canada reach their greenhouse gas emission reduction goals. In this paper, we assess the cost-effectiveness of the Site C project using a detailed linear programming capacity expansion and dispatch model of the British Columbia and Alberta electricity systems. Our model includes hourly electricity demand data for sixteen sub-regions within BC and Alberta, hourly wind resource data for 681 grid cells in the two provinces, hourly solar irradiation data from 112 weather stations to characterize the solar resource, and a detailed hydroelectric model that accounts for the chaining of hydroelectric facilities and reservoirs within the Peace River, Columbia River, Kootenay River and Bridge River watersheds. We assess the value of the Site C project by calculating the optimal electricity system for supplying BC and Alberta electricity demand in 2030 both with and without the presence of the Site C project. Our model simulations reveal that under current electricity sector policies, the Site C project is uneconomic. We find that the value created by Site C only exceeds its costs in scenarios where BC and Alberta coordinate electricity markets, build additional transmission capacity, and work to reduce greenhouse gas emissions significantly below baseline levels.