Analyzing the impact of the 5CP Ontario peak reduction program on large consumers

Peak reduction is an important problem in the context of the electricity grid and has led to conservation programs in various jurisdictions. For example, in Ontario, Canada, residential customers are charged higher prices during peak times, while large industrial and commercial customers pay heavy surcharges that depend on their load during Ontario’s five peak-demand days. Reducing these surcharges is a challenging problem for large consumers due to the difficulty of predicting peak days in advance.We study the impact of this peak reduction program, called 5 Coincident Peaks (5CP), on consumers by analyzing the difficulty of predicting peak-demand days and peak hours on those days. We find that even the state-of-the art peak-prediction algorithms require consumers to curtail load ten or more times, and even then, they may not identify all five peak-demand days. We also analyze alternative policies that cold help reduce peak demand in Ontario.     

Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour

Smart-metering allows electricity utilities to provide consumers with better information on their energy usage and to apply time-of-use pricing. These measures have been shown to reduce electricity consumption and induce time-shifting of demand. Less is known about how they affect residential energy efficiency investment behaviour. We use data from a randomised-controlled trial on a sample of almost 2500 Irish consumers, conducted over a 12-month period to investigate the effect of smart-metering and residential feedback on household investment behaviour. The results show that exposure to time-of-use pricing and information stimuli, while reducing overall and peak usage, can also have the unintended effect of reducing investment in energy efficiency measures within the home. Our findings indicate that households exposed to treatment were less likely to adopt any energy saving measure (23–28 % on average), and those households adopted less energy saving features than those in the control group (15–21 % on average). This result highlights the potential for behavioural interventions to have unintended consequences on behaviours other than those specifically targeted. Furthermore, it underlines the importance of examining a wider range of outcomes and allowing longer time-scales when evaluating this type of experiment.     

The effect of sociodemographic diversity of residential customers on the financial risk experienced in the retail electricity market

The sociodemographic diversity of residential customers can affect the level of financial risk that an electricity provider experiences in the retail market. To demonstrate the relationship between sociodemographic diversity and financial risk, electricity consumption data drawn from the United Kingdom Power Networks ‘Low Carbon London’ project was analyzed to explore the relationship between sociodemographic diversity and financial risk experienced by electricity retailers. The results show that when increasing the sociodemographic diversity amongst a group of residential customers the effect on financial risk depends on the electricity consumption patterns of individual customers and the relationship of consumption patterns between residential customers. Increasing sociodemographic diversity amongst residential customers with very distinct energy consumption patterns can decrease the overall financial risk associated with the aggregated revenue received from these customers. However, the results showed that adding customers to a customer base without consideration for their sociodemographic background can cause the overall financial risk associated with the aggregated revenue received to change erratically. Whilst previous studies have considered customer diversity and its influence on peak electricity demand, this research advances the state-of-the-art by showing the importance of customer diversity to the financial quantity risk experienced by electricity retailers. This finding has serious implications for electricity providers seeking to mitigate financial risk in the retail electricity market.     

Energy efficiency standards for refrigerators in Brazil: A methodology for impact evaluation

In Brazil energy efficiency standards for cold appliances was established in 2007. A specified single set of MEPS (minimum energy performance standards) for refrigerators, freezers and freezer refrigerators was implemented without evaluating its impacts and estimation of potential electricity savings. This paper presents a methodology for assessing the impacts of the Brazilian MEPS for cold appliances. It uses a bottom-up approach to estimate residential end-use consumption and to evaluate the energy saving potential for refrigerators. The household electricity consumption is projected by modeling appliance ownership using an econometric approach based on the recent household survey data. A cost–benefit analysis for more stringent standards is presented from the perspective of the society and electricity customers. The results showed that even considering the current market conditions (high discount rate for financing new efficient equipment) some MEPS options are advantageous for customers. The analysis also demonstrates significant cost-effective saving potential from the society perspective that could reach 21 TWh throughout the period of 2010–2030—about 25% of current residential consumption.     

A novel peak load shaving algorithm via real‐time battery scheduling for residential distributed energy storage systems

As population grows and energy consumption increases, generation, transmission, and energy distribution costs also increase. Sudden and unpredicted demand increase at peak periods might lead to failure and even damage the power grid. This is a challenge for stability and reliability of the grid. Peak load shaving is considered as an effective approach while transition from peak load periods. In this paper, peak load shaving is modeled mathematically through storing energy on demand side and solved using optimization method. Using the results obtained from solving the optimization problem, a simple effective algorithm is proposed for peak load shaving via real‐time scheduling of distributed battery storage systems without complicated calculations. All characteristics required for systemic design of peak load shaving for residential, commercial, and industrial loads are presented. This method can be used in the presence of photovoltaic arrays or other renewable or nonrenewable distributed energy resources simultaneously, and it can be adapted to different conditions and demands. Here, real measured data of a residential state, an office building with photovoltaics, a hotel, and a small office are used for simulation, and GAMS is used for analysis.     

Modeling the impact of integrating solar thermal systems and heat pumps for domestic hot water in electric systems – The case study of Corvo Island

The use of solar thermal systems with electricity backup and heat pumps as hot water suppliers in residential buildings seems to be a very promising way to increase energy efficiency. Nevertheless, the massive adoption of such solutions in small networks (neighborhood, village) may induce problems in the electric grid management. This study explores the impact of such systems in small electric grids, using an hourly electricity backup load model. To test and validate the model, we used the island of Corvo (Azores), a small isolated community where it is being implemented a project of electrification of domestic hot water systems (DHW). We consider different load scenarios to manage the backup of DHW systems and analyze its consequences on the peak load and overall energy demand. For Corvo, for the best case where the backup is limited and distributed along off-peak hours, we observed an increase of 24% in the peak load and 7.5% in the annual energy demand. Critical values of peak load are found in winter, when daily solar irradiation is lower than 2000 Wh/m²/day. We conclude that the solar thermal systems are responsible for most of the peak load increase, but since they have the flexibility to adjust the electric backup hours due to the thermal storage capacity, the use of these systems can minimize the impact on the grid. Heat pumps on the other hand, albeit being more efficient in terms electric backup, are less flexible to contribute to the grid management as they operate continuously.     

Fostering economic demand response in the Midwest ISO

The Midwest Independent System Operator (MISO) runs the power grid in 14 states and one Canadian province and has a peak demand of some 116,000 MW. Its operational area is richly supplied with reliability-triggered demand response programs such as direct load control of residential appliances and curtailable/interruptible rates for commercial and industrial customers. However, economic demand response programs are lacking. This paper discusses three different ways in which such programs can be introduced in the wholesale energy markets run by MISO. These include, first, an approach in which utilities and load serving entities move retail customers to dynamic pricing and other time-based pricing rates; second, an approach in which these same entities and possibly third-parties bid price responsive demand curves into the wholesale market; and third, an approach in which demand response is bid as a supply resource into the wholesale market.     

Economic optimization and sensitivity analysis of photovoltaic system in residential buildings

This paper deals with the problem of optimal size of grid-connected photovoltaic (PV) system for residential application. It is assumed the PV system can input or output liberally the electricity to the utility electricity grid. A simple linear programming model is developed. The objective is to minimize the annual energy cost of a given customer, including PV investment cost, maintenance cost, utility electricity cost, subtracting the revenue from selling the excess electricity. The model reports the optimal PV capacity that customers adopt with their electricity requirements. Using this model, an investigation is conducted of economically optimal PV investment under several conditions for a typical residential building. Additionally, the sensitivity of levelized cost and simple payback period to various economic and technical circumstances has been analyzed.     

Viability of off-grid electricity supply using rice husk: A case study from South Asia

Rice husk-based electricity generation and supply has been popularized in South Asia by the Husk Power Systems (HPS) and the Decentralised Energy Systems India (DESI), two enterprises that have successfully provided electricity access using this resource. The purpose of this paper is to analyze the conditions under which a small-scale rural power supply business becomes viable and to explore whether larger plants can be used to electrify a cluster of villages. Based on the financial analysis of alternative supply options considering residential and productive demands for electricity under different scenarios, the paper shows that serving low electricity consuming customers alone leads to part capacity utilization of the electricity generation plant and results in a high cost of supply. Higher electricity use improves the financial viability but such consumption behaviour benefits high consuming customers greatly. The integration of rice mill demand, particularly during the off-peak period, with a predominant residential peak demand system improves the viability and brings the levelised cost of supply down. Finally, larger plants bring down the cost significantly to offer a competitive supply. But the higher investment need and the risks related to monopoly supply of husk from the rice mill, organizational challenges of managing a larger distribution area and the risk of plant failure can adversely affect the investor interest. Moreover, the regulatory uncertainties and the potential for grid extension can hinder business activities in this area.     

Hedging strategies in energy markets: The case of electricity retailers

As market intermediaries, electricity retailers buy electricity from the wholesale market or self-generate for re(sale) on the retail market. Electricity retailers are uncertain about how much electricity their residential customers will use at any time of the day until they actually turn switches on. While demand uncertainty is a common feature of all commodity markets, retailers generally rely on storage to manage demand uncertainty. On electricity markets, retailers are exposed to joint quantity and price risk on an hourly basis given the physical singularity of electricity as a commodity. In the literature on electricity markets, few articles deal on intra-day hedging portfolios to manage joint price and quantity risk whereas electricity markets are hourly markets. The contributions of the article are twofold. First, we define through a VaR and CVaR model optimal portfolios for specific hours (3 am, 6 am,. . . ,12 pm) based on electricity market data from 2001 to 2011 for the French market. We prove that the optimal hedging strategy differs depending on the cluster hour. Secondly, we demonstrate the significantly superior efficiency of intra-day hedging portfolios over daily (therefore weekly and yearly) portfolios. Over a decade (2001–2011), our results clearly show that the losses of an optimal daily portfolio are at least nine times higher than the losses of optimal intra-day portfolios.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

An exploratory analysis of California residential customer response to critical peak pricing of electricity

This paper summarizes the results from an exploratory analysis of residential customer response to a critical peak pricing (CPP) experiment in California, in which 15 times per year participating customers received high price signals dispatched by a local electricity distribution company. The high prices were about three times the on-peak price for the otherwise applicable time-of-use rate. Using hourly load data collected during the 15-month experiment, we find statistically significant load reduction for participants both with and without automated end-use control technologies. During 5-h critical peak periods, participants without control technology used up to 13% less energy than they did during normal peak periods. Participants equipped with programmable communicating thermostats used 25% and 41% less for 5 and 2 h critical events, respectively. Thus, this paper offers convincing evidence that the residential sector can provide substantial contributions to retail demand response, which is considered a potential tool for mitigating market power, stabilizing wholesale market prices, managing system reliability, and maintaining system resource adequacy.     

Demand-side management by electric utilities in Switzerland: Analyzing its impact on residential electricity demand

In this paper, we use panel data from a survey conducted on 30 utilities in Switzerland to estimate the impact of demand-side management (DSM) activity on residential electricity demand. Using the variation in DSM activity within utilities and across utilities over time we identify the impact of DSM programs and find that their presence reduces per customer residential electricity consumption by around 5%. If we consider monetary spending, the effect of a 10% increase in DSM spending causes a 0.14% reduction in per customer residential electricity consumption. The cost of saving a kilowatt hour is around 0.04CHF while the average cost of producing and distributing electricity in Switzerland is around 0.18CHF per kilowatt hour. We conclude that current DSM practices in Switzerland have a statistically significant effect on reducing the demand for residential electricity.     

Short- and long-run time-of-use price elasticities in Swiss residential electricity demand

This paper presents an empirical analysis on the residential demand for electricity by time-of-day. This analysis has been performed using aggregate data at the city level for 22 Swiss cities for the period 2000−2006. For this purpose, we estimated two log–log demand equations for peak and off-peak electricity consumption using static and dynamic partial adjustment approaches. These demand functions were estimated using several econometric approaches for panel data, for example LSDV and RE for static models, and LSDV and corrected LSDV estimators for dynamic models. The attempt of this empirical analysis has been to highlight some of the characteristics of the Swiss residential electricity demand. The estimated short-run own price elasticities are lower than 1, whereas in the long-run these values are higher than 1. The estimated short-run and long-run cross-price elasticities are positive. This result shows that peak and off-peak electricity are substitutes. In this context, time differentiated prices should provide an economic incentive to customers so that they can modify consumption patterns by reducing peak demand and shifting electricity consumption from peak to off-peak periods.     

The effect of merger and consolidation activities on the efficiency of electricity distribution regions in Turkey

In this study, we analyze the efficiency performances of Turkish electricity distribution companies throughout 2002 and 2009 in the stochastic frontier analysis (SFA) framework. Our more specific goal is to measure how the efficiency performances of the electricity distribution regions were affected by the mergers between distribution regions occurred in 2005. The results demonstrate that efficiencies of the companies serving to mostly the residential customers are higher than those of other companies. However, the positive impact of residential customers on the efficiencies decreased significantly following the mergers between electricity distribution companies. In contrast, the positive impact of higher customer density on efficiency also decreased, but insignificantly following the restructuring activities in the market in 2005. Our study also witnesses that the positive impact of the mergers on the efficiencies decreases as the proportion of sales to residential customers in regions increases. In contrast, the restructuring activities of 2005 increased equally the efficiencies of the companies serving in the regions with higher and lower customer densities.     

Optimal operational strategy for a future electricity and hydrogen supply system in a residential area

This study introduces a novel framework of an electricity and hydrogen supply system integrating with a photovoltaic power station for a residential area. The non-residential parts including the power grid and non-residential vehicles are added to ensure power balance and bring benefits, respectively. The optimal operational strategy of the proposed framework with considering uncertainties is proposed. The objective function minimizes the expected operational cost (EOC) by reducing the imported electricity from the power grid and increasing exported electricity/hydrogen to non-residential vehicles. Additionally, the demand response program (DRP) is applied in the residential load to achieve operational cost reduction. The uncertainties are modeled via various scenarios by using scenario-based stochastic optimization method. Notably, existing research for similar frameworks both lacks the consideration of uncertainties and DRP, and fails to distinguish the residential and non-residential vehicles with different charging behaviors. The results indicate that 1) The feasibility of the proposed framework is validated which can ensure the power balance of the residential area and reduce the operational cost. 2) The EOC is reduced when considering DRP.     

Examination of optimum benefits of customer and LSE by incentive and dynamic price-based demand response

ABSTRACT

The balancing approach of electricity generation and consumption is an essential part of a reliable power system. The mismatch between supply and demand may also cause fluctuation in the power system. This study proposes an incentive and penalty-based demand response (I&P-DR) program for improving the profitability of both LSE and customers. First, we use a parameter that weighs the financial benefits of LSE and customers and provides considerable economic benefits to both sides. Second, an incentive and penalty (I&P) price scheme have been employed to recompense and penalize customers and reduce the electricity demand at peak hours. Finally, the study analyzes the importance of (I&P-DR) and its impact on customers’ sensitivity during peak intervals. Simulation results showed that the flexibility to consumption can be brought through the application of the proposed (I&P-DR) program and also provide the financial benefits to both, customers and LSEs.     

An analysis of a demand charge electricity grid tariff in the residential sector

This paper analyzes the demand response from residential electricity consumers to a demand charge grid tariff. The tariff charges the maximum hourly peak consumption in each of the winter months Dec, Jan, and Feb, thus giving incentives to reduce peak consumption. We use hourly electricity consumption data from 443 households, as well as data on their grid and power prices, the local temperature, wind speed, and hours of daylight. The panel data set is analyzed with a fixed effects regression model. The estimates indicate average demand reductions up to 0.37 kWh/h per household in response to the tariff. This is on average a 5% reduction, with a maximum reduction of 12% in hour 8 in Dec. The consumers did not receive any information on their continuous consumption or any reminders when the tariff was in effect. It is likely that the consumption reductions would have been even higher with more information to the consumers.     

Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models

Electric utilities and regulators face difficult challenges evaluating new energy efficiency and smart grid programs prompted, in large part, by recent state and federal mandates and financial incentives. It is increasingly difficult to separate electricity use impacts of individual utility programs from the impacts of increasingly stringent appliance and building efficiency standards, increasing electricity prices, appliance manufacturer efficiency improvements, energy program interactions and other factors. This study reviews traditional approaches used to evaluate electric utility energy efficiency and smart-grid programs and presents an agent-based end-use modeling approach that resolves many of the shortcomings of traditional approaches. Data for a representative sample of utility customers in a Midwestern US utility are used to evaluate energy efficiency and smart grid program targets over a fifteen-year horizon. Model analysis indicates that a combination of the two least stringent efficiency and smart grid program scenarios provides peak hour reductions one-third greater than the most stringent smart grid program suggesting that reductions in peak demand requirements are more feasible when both efficiency and smart grid programs are considered together. Suggestions on transitioning from traditional end-use models to agent-based end-use models are provided.     

Residential implementation of critical-peak pricing of electricity

This paper investigates how critical-peak pricing (CPP) affects households with different usage and income levels, with the goal of informing policy makers who are considering the implementation of CPP tariffs in the residential sector. Using a subset of data from the California Statewide Pricing Pilot of 2003–04, average load change during summer events, annual percent bill change, and post-experiment satisfaction ratings are calculated across six customer segments, categorized by historical usage and income levels. Findings show that high-use customers respond significantly more in kW reduction than do low-use customers, while low-use customers save significantly more in percentage reduction of annual electricity bills than do high-use customers—results that challenge the strategy of targeting only high-use customers for CPP tariffs. Across income levels, average load and bill changes were statistically indistinguishable, as were satisfaction rates—results that are compatible with a strategy of full-scale implementation of CPP rates in the residential sector. Finally, the high-use customers earning less than $50,000 annually were the most likely of the groups to see bill increases—about 5% saw bill increases of 10% or more—suggesting that any residential CPP implementation might consider targeting this customer group for increased energy efficiency efforts.