Reduce Electricity Demand Research Articles

Reducing electricity demand peaks on large-scale dairy farms

As countries seek to meet climate change mitigation obligations and integrate high levels of variable renewable generation into electricity systems, opportunities for flexible demand are increasingly being sought to provide a cost-effective approach to balancing electricity grids. In New Zealand, the dairy farming sector has the capacity to shift demand outside of peak periods and balance grid demand. We assess the potential for a group of six large-scale New Zealand dairy farms to provide electricity demand flexibility by analysing their electricity demand patterns based on one year of 30 min resolution electricity data. Daily profiles of electricity demand are analysed to demonstrate that irrigation and milking related electricity consumption contribute to peaks. Under a proposed time-of use (TOU) pricing model, 20% of electricity costs in the dairy shed result from the morning peak period. Flattening demand would reduce electricity costs for milking by 3.3% as more of the demand would be overnight and ‘off-peak’. TOU changes to irrigation were found to make responding to peaks more difficult where at least 600 kW had previously been available for load shedding during the irrigation season. If farmers were able to alter their practices or utilise energy storage technologies in response to time-of-use pricing they could reduce energy costs and reduce their impact on peaks in demand on the electricity network.

Eskom’s COVID-19 force majeure

Learning outcomes The case describes the fall of Eskom, which in 2001 was named the Financial Times’ Power Company of the Year, but by 2019 was suffering from “systemic corruption, malfeasance, fraud and state capture” that had “compromised the credibility of the organisation and eroded investor confidence”. Eskom’s incompetent management lays the ground for reasonable doubt as to whether the force majeure notice was indeed irresistible. The case suggests several methods available in financial markets to hedge risk – but to what extent are these relevant and appropriate? The main objective of the case, however, is to examine and assess the criteria required to claim force majeure. Two aspects are questionable: Was the virus unforeseeable and was it irresistible? Eskom is “bleeding” R2.5m per month because of significantly reduced electricity demand, and while it clearly benefits Eskom to break their supply contract, the consequences for Exxaro are far more dire. And, if carried to conclusion, how would such actions impact the entire economy? Case overview/synopsis In April 2020 South Africa’s stated-owned electricity utility Eskom sent a pre-cautionary force-majeure notification to Exxaro Limited’s Grootegeluk Coal Mine. The notification, citing COVID-19 as an unforeseeable, external and irresistible event, would have disastrous consequences for the mine’s 25 m tonnes pa coal contract to supply Eskom’s Medupi power station. Not only was the legality of the force-majeure questionable, it was unethical, and not in the spirit of President Ramaposa’s call to businesses to continue paying contractors. The case briefly describes Eskom’s troubled history following South Africa’s 1994 democratic election. It examines the force majeure clause common in contracts, and questions whether COVID-19 meets the criteria of an “unforeseeable, external and irresistible” event. Complexity academic level MBA and Executive Education Supplementary materials Teaching Notes are available for educators only. Subject code CSS 7: Management Science.

Unlocking the Impacts of COVID-19 Lockdowns: Changes in Thermal Electricity Generation Water Footprint and Virtual Water Trade in Europe.

Drastic changes in electricity demand have been observed since March 2020 in Europe, after several countries implemented lockdown-like measures to contain the spread of COVID-19. We investigate the sensitivity of the electricity–water nexus in the European electric grid to large-scale behavior changes during the COVID-19 pandemic lockdown-like measures. We quantify changes in the blue virtual water trade between five European countries heavily affected by COVID-19 during the same period. As a result, the consumptive water footprint of thermal power plant operations in Europe decreased by 1.77 × 106 m3/day during the COVID-19 lockdowns, compared to the average of the past four years. Reduced electricity demand accounts for 16% (0.29 × 106 m3/day) of the decrease, while the remainder is attributable to changes in the electricity generation mix toward less water-intensive technologies before 2020 and during lockdowns. Virtual water transfers associated with electricity were also affected: Italy, a hotspot of COVID-19, reduced its water footprint by 8.4% and its virtual water imports by 70,700 m3/day. Germany and France slightly reduced their domestic water footprint of electricity but increased their virtual water imports. These findings improve our understanding of the impacts of large-scale behavior and technological changes to the European electricity–water nexus.

Air-conditioning demand response resource assessment for Australia

It is widely acknowledged that demand response must play an increasingly important role in future electricity markets. This is as much due to increasing demand for air-conditioning as it is due to the changing sources of generation. In the National Electricity Market (NEM) in Australia, for just 3% of the year demand increases by 20%, or the equivalent of the generation from the two largest coal fired power stations combined. Over smaller regions and time intervals demand spikes are larger. Air-conditioning demand response takes advantage of a buildings inherent thermal storage to reduce electricity demand at peak times with no discernible impact on the indoor operating environment. Here we estimate the existing potential of commercial and residential air-conditioning systems to provide demand response across Australia. We use a top-down approach based on disaggregation of electricity substation half-hourly data. Results are that peak (i.e. 99.5th percentile) demand on the NEM could be reduced by up to 5.8% or 1.2 GW with the time of day at which the peak occurs delayed by approximately 2 hours. Based on the timing of the available capacity, both residential and commercial buildings are suited to providing air-conditioning demand response.

Modified deep learning and reinforcement learning for an incentive-based demand response model

Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security.

An Assessment of CO2 Mitigation Options for Residential Electricity Consumption in the Kingdom of Saudi Arabia: A Case Study of Al-Qassim

This study examined the effectiveness of selected measures for reducing the final electricity demand of Al-Qassim’s residential sector and the resulting CO2 emissions. Accordingly, the Low Emission Analysis Platform (LEAP) was used to develop reference and mitigation scenarios for the period 2018–2030 based on the results of the national household energy survey. Several mitigation measures were explored, including improving the efficiency of air conditioners, installing residential solar panels, using solar water heaters, and raising public awareness regarding electricity conservation, both as stand-alone and combined mitigation scenarios. An analysis of the results of the household energy survey revealed the continuing projected rise in the final electricity demand, reaching 12.4 thousand GWh by 2030, with CO2 emissions exceeding 7.4 million tons. The results of probabilistic modeling using the survey results indicated that by 2030, the final electricity demand of the residential sector in Al-Qassim will range between 6.5 and 19.5 thousand GWh at a confidence rate of 90%, with minimum and maximum demands of 4.8 and 32.8 thousand GWh, respectively. The study’s findings also suggest that implementing the mitigation options on stand-alone basis can achieve 15% cumulative reduction in the final electricity demand and CO2 emissions, and 19% when implemented as combined scenarios during 2022-2030 compared with the reference scenario. This study recommends conducting a feasibility study for implementing these mitigation measures and exploring different financing models. A further recommendation is to collect additional data on electricity consumption patterns within the residential sector during the household energy survey’s implementation, to ensure the provision of inputs into the assessment of energy and climate change policies targeting the residential sector.

Electricity load dynamics, temperature and seasonality Nexus in Algeria

During the last decade, major changes have affected the electricity sector in Algeria. As consequence it recorded an important increase of electricity demand in energy and capacity under the mixt pressure of demography and socio-economic development, climate change, and the depletion of natural gas reserves which imposes new challenges in terms of renewable energies and demand side management. These adjustments enhanced the important role of electricity demand forecasting. The aim of this study is to construct the best overall model that represents the relationship between electricity demand and air temperature, i.e. heating and cooling degree days, and taking into consideration other deterministic variables. A simple multiple regression model has been developed, since it allows us to investigate this relationship in an easy and controlled manner. Additionally, the model can be used to forecast electricity demand for the next year on a daily basis. The results show that CDD and HDD have the highest effect on electricity demand, and can be seen as the main factors affecting the daily load in Algeria. On the other hand, holidays reduce electricity demand in all seasons. We also secluded the dummy representing the holy month of Ramadan where it was clear that the behavior during this holiday increased the electricity demand especially in the summer seasons.

Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty

Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization.

Energy self-sufficient households with photovoltaics and electric vehicles are feasible in temperate climate.

The idea that households produce and consume their own energy, that is, energy self-sufficiency at a very local level, captures the popular imagination and commands political support across parts of Europe. This paper investigates the technical and economic feasibility of household energy self-sufficiency in Switzerland, which can be seen as representative for other regions with a temperate climate, by 2050. We compare sixteen cases that vary across four dimensions: household type, building type, electricity demand reduction, and passenger vehicle use patterns. We assume that photovoltaic (PV) electricity supplies all energy, which implies a complete shift away from fossil fuel based heating and internal combustion engine vehicles. Two energy storage technologies are considered: short-term storage in lithium-ion batteries and long-term storage with hydrogen, requiring an electrolyzer, storage tank, and a fuel cell for electricity conversion. We examine technological feasibility and total system costs for self-sufficient households compared to base cases that rely on fossil fuels and the existing power grid. PV efficiency and available rooftop/facade area are most critical with respect to the overall energy balance. Single-family dwellings with profound electricity demand reduction and urban mobility patterns achieve self-sufficiency most easily. Multi-family buildings with conventional electricity demand and rural mobility patterns can only be self-sufficient if PV efficiency increases, and all of the roof plus most of the facade can be covered with PV. All self-sufficient cases are technically feasible but more expensive than fully electrified grid-connected cases. Self-sufficiency may even become cost-competitive in some cases depending on storage and fossil fuel prices. Thus, if political measures improve their financial attractiveness or individuals decide to shoulder the necessary investments, self-sufficient buildings may start to become increasingly prevalent.

Cost-effectiveness of energy efficiency investments for high renewable electricity systems

Although several studies have recognized the challenges brought about the high integration of Renewable Energy Sources (RES), research has yet to systematically investigate the effects of different levels of Energy Efficiency Measures (EEMs) on power systems with a high share of RES. The primary aim of this paper is to quantitatively evaluate the economic impact of different levels of energy efficiency investments in systems with a high share of RES, based on the case of Brazil. Our review analysis revealed a lack of understanding of how to calculate the Long-term Average Cost of Saved Electricity (LACoSE). Therefore, the methodological contribution of this research also lies in the proposition of a step-by-step procedure to estimate this variable. Findings of this study suggest that EEMs may result in a reduction in the expected new installed capacity between 1.9% and 10.3%, and from 0.4% to 4.3% for the total CO2 emissions (for reductions in the final electricity demand varying from 1% to 5% respectively). This comes with a decrease in the overall system costs between 1.9% and 7.7%. The results provide further support for the hypothesis that the economic impacts of energy efficiency investments and its cost-effectiveness are affected by the share of RES in the power system and these type of measures may provide holistic benefits including not only economic advantages but also environmental and social improvements. The proposed methodology largely extends beyond the case addressed as it may provide valuable lessons for other electricity systems.

DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?

Abstract: To date, research has mostly focused on the impact of energy efficiency on the total electricity demand but not on the electricity demand profiles. To address this gap, we estimate the impact of energy efficiency measures and policies such as minimum energy performance standards on the peak load by developing a bottom-up model that generates Swiss household hourly electricity demand profiles per appliance based on time use data. The model estimates that evening appliance peak demand can be reduced by 38% when the appliances are replaced by the highest energy efficiency label available on market. We find that changing light bulbs to LED would have the same peak reduction as switching cooking or wet appliances to off-peak periods throughout the year. We also show that the evening appliance peak demand could reduce in 2035 by 24% thanks to the improvement of the energy performance of the stock. Cooking appliances, the least favourable appliances to be involved in demand response, is expected to be the highest contributors to the evening peak in 2035. Our findings show that policy makers should pay due attention to energy efficiency improvement not only for reducing electricity demand but also in order to reduce peak load.

Reaching New Lows? The Pandemic's Consequences for Electricity Markets

The large reductions in electricity demand caused by the COVID-19 crisis have disrupted electricity systems worldwide. This article draws insights from New York into the consequences of the pandemic for electricity markets. It disentangles the effects of the demand reductions, increased forecast errors, and fuel price drops on the day-ahead and real-time markets. From March 16 to May 31, New York has experienced a 6.5% demand reduction, prices have dropped, and producers have lost $87 million (-18%). This estimate extrapolates to $2.6 billion for the entire US. Looking forward, these new lows signal the needs for market design adjustments.

Firms and Households During the Pandemic: What Do We Learn from their Electricity Consumption?

We analyze the impact of the COVID-19 pandemic on electricity consumption patterns in Spain. We highlight the importance of decomposing total electricity consumption into consumption by firms and by households to better understand the economic and social impacts of the crisis. While electricity demand by firms has fallen substantially, the demand by households has gone up. In particular, during the total lockdown, these effects reached -29% and +10% respectively, controlling for temperature and seasonality. While the electricity demand reductions during the second wave were milder, the demand by firms remained 5% below its normal levels. We also document a change in people’s daily routines in response to the stringency of the lockdown measures, as reflected in their hourly electricity consumption patterns.

Reshaping Electricity Demand During COVID-19 Containment Measures

The New Zealand government introduced strict containment measures in response to the Covid-19 virus. We assess the impact of containment measures on wholesale electricity demand using the augmented auto-regressive-moving-average model. The Alert Level-4 lockdown had the largest, significant and negative effect on electricity demand compared to other containment level measures. Alert Level 4 resulted in a 15.5 % reduction in wholesale electricity demand. Structural breaks in the data are evident as containment progressed to Alert Level 1.

Optimal Demand Response Management of a Residential Microgrid Using Model Predictive Control

Demand response (DR) is an important factor contributing to achieve a balance between energy production and demand in Smart Grids. DR plays a key role in the use of residential energy allowing to improve the load management, electrical grid reliability, to reduce energy demand during peak hours and to minimize the use of energy in face of increasing energy prices. This article proposes a Model Predictive Control (MPC) strategy to manage the energy resources of a residential microgrid combined with DR techniques, such as load curtailment, that promotes short term reduction of electricity demand in pre-defined hours. In particular, the presented approach encompasses degradation issues of the Energy Storage System (ESS), the cost of the electricity, renewable energy generation, and other operational constraints. The developed control strategy is able to maximize microgrid economical benefit, while minimizing the degradation of the ESS, reducing electricity consumption during the day, and fulfilling the different operational constraints. The proposed strategy is validated in an experimental renewable-energy based microgrid platform for different climatic conditions. The obtained results demonstrate and verify the effectiveness of the proposed control and management strategy.

Performance analysis of solar assisted ground coupled heat pump system in Latvia

Today, with the increased popularity and availability of renewable energy sources the question of ensuring a proper household heating, that is energy, is not as complicated as it used to be. Users are embracing the environmental sustainability stand and are turning to heat pumps and solar panels as means of providing heat for their homes. The geothermal heat pump and solar thermal panels energy efficiency rates coupled make these the perfect choice for those, who are concerned about the impact they make on the environment-aspiring to zero emission buildings. The combined system of solar thermal panels and ground source heat pump for heating capacity 13 kW will be developed. The main novelty is the investigation of a new type of a solar thermal accumulation tank. Hypothetically this solar assisted ground coupled heat pump system should reach COP rate 4 and reduce electricity demand consumption by 40%. To find out the truth of proposed thesis, the research will be carried out. Both simulation and real-life object data analysis will be performed. As a result, the potential of such a system in northern latitudes will be determined.

Private Versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration and Cost Impacts

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly-evolving electricity systems. Here we examine these interactions using a U.S.-wide simulation framework encom- passing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that an SAEV fleet 9% the size of today’s active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly lowering GHG emissions and decreasing solar curtailment by about one- third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Evaluation of Dynamic Insulation Systems for Residential Buildings in Barcelona, Spain

Abstract This paper evaluates the potential energy cost savings when high R-value static insulation layers as well as dynamic insulation materials (DIMs) are applied to residential housing located in Barcelona Spain. The analysis considers three dwelling prototypes to characterize the existing housing stock in Barcelona including detached attached and apartments. In addition three vintages for each housing prototype are defined: before 1979 when building envelope insulation took effect in Spain between 1980 and 2006 and after 2006 when the building envelope insulation code became more restrictive. Using a modified 3R2C network model to determine thermal loads the performance of both static and dynamic insulation systems is evaluated when applied to exterior wall for various housing prototypes in Barcelona. The dynamic insulation R-value is selected based on a 2-step control strategy. The analysis results indicate that DIMs with the largest R-value step (i.e. difference between the high and the low R-values) achieve the highest savings in source energy reaching up to 19% reduction in source heating and cooling energy for the entire housing stock of Barcelona. The annual energy savings achieved by DIMs are valued to be 181 M€/year for the entire existing housing stock in Barcelona. In addition electrical peak demand reduction associated with retrofitting exterior walls for the existing Barcelona housing stock can result in future avoidance of building new power plants and can provide additional 144 M€ and 162 M€ for respectively static and dynamic insulation systems. Considering the current energy mix applying dynamic wall insulation systems for Barcelona existing housing stock could reduce annual CO2 emissions by more than 300 000 tons or 6.80% of the total carbon dioxide currently emitted to heat and cool homes.

Performance investigation of a SOFC-primed micro-combined hybrid cooling and power system in hot and humid regions

The provision of cooling in a high-capacity trigeneration system is challenging due to the huge difference in the energy performance between heat-driven and electricity-driven cooling equipment. A micro-scale application can be more attractive, particularly when the recovered waste heat is solely used to drive a desiccant dehumidification cycle in a hybrid cooling system which offers better energy performance for use in hot and humid regions. Thus, given the availability of small-capacity solid oxide fuel cells (SOFCs), a SOFC-primed micro-combined hybrid cooling and power (mCHyCP) system was developed, and its performance in a 24-h restaurant in Singapore and in Hong Kong was analyzed. It was found that by performing year-round dynamic simulations using TRNSYS, a 10% reduction in the total electricity demand could be achieved in the tropical Singaporean climate compared to a 5% reduction in demand in the sub-tropical Hong Kong climate. This reflected the effect of different seasons on the performance of the mCHyCP system. In addition, at most 24% and 38% reductions in the primary energy consumption and carbon dioxide emissions for both regions could be achieved, which indicated the potential of such system as a sustainable distributed energy source.

Ensuring statistics have power: Guidance for designing, reporting and acting on electricity demand reduction and behaviour change programs

In this paper we address ongoing confusion over the meaning of statistical significance and statistical power in energy efficiency and energy demand reduction intervention studies. We discuss the role of these concepts in designing studies, in deciding what can be inferred from the results and thus what course of subsequent action to take. We do this using a worked example of a study of Heat Pump demand response in New Zealand to show how to appropriately size experimental and observational studies, the consequences this has for subsequent data analysis and the decisions that can then be taken. The paper then provides two sets of recommendations. The first focuses on the uncontroversial but seemingly ignorable issue of statistical power analysis and sample design, something regularly omitted in the energy studies literature. The second focuses on how to report energy demand reduction study or trial results, make inferences and take commercial or policy-oriented decisions in a contextually appropriate way. The paper therefore offers guidance to researchers tasked with designing and assessing such studies; project managers who need to understand what can count as evidence, for what purpose and in what context and decision makers who need to make defensible commercial or policy decisions based on that evidence. The paper therefore helps all of these stakeholders to distinguish the search for statistical significance from the requirement for actionable evidence and so avoid throwing the substantive baby out with the p-value bathwater.