Price,environment and security: Exploring multi-modal motivation in voluntary residential peak demand response

Peak demand on electricity grids is a growing problem that increases costs and risks to supply security. Residential sector loads often contribute significantly to seasonal and daily peak demand. Demand response projects aim to manage peak demand by applying price signals and automated load shedding technologies. This research investigates voluntary load shedding in response to information about the security of supply, the emission profile and the cost of meeting critical peak demand in the customers’ network. Customer willingness to change behaviour in response to this information was explored through mail-back survey. The diversified demand modelling method was used along with energy audit data to estimate the potential peak load reduction resulting from the voluntary demand response. A case study was conducted in a suburb of Christchurch, New Zealand, where electricity is the main source for water and space heating. On this network, all water heating cylinders have ripple-control technology and about 50% of the households subscribe to differential day/night pricing plan. The survey results show that the sensitivity to supply security is on par with price, with the emission sensitivity being slightly weaker. The modelling results show potential 10% reduction in critical peak load for aggregate voluntary demand response.     

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.     

Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers

To the extent that demand response represents an intentional electricity usage adjustment to price changes or incentive payments, consumers who exhibit more-variable load patterns on normal days may be capable of altering their loads more significantly in response to dynamic pricing plans. This study investigates the variation in the pre-enrollment load patterns of Korean commercial and industrial electricity customers and their impact on event-day loads during a critical peak pricing experiment in the winter of 2013. Contrary to conventional approaches to profiling electricity loads, this study proposes a new clustering technique based on variability indices that collectively represent the potential demand–response resource that these customers would supply. Our analysis reveals that variability in pre-enrollment load patterns does indeed have great predictive power for estimating their impact on demand–response loads. Customers in relatively low-variability clusters provided limited or no response, whereas customers in relatively high-variability clusters consistently presented large load impacts, accounting for most of the program-level peak reductions. This study suggests that dynamic pricing programs themselves may not offer adequate motivation for meaningful adjustments in load patterns, particularly for customers in low-variability clusters.     

Option value of electricity demand response

As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.     

Households’ self-selection of dynamic electricity tariffs

Offering electricity consumers time-differentiated tariffs may reduce peak consumption if consumers choosing the tariffs are demand responsive. However, one concern is that time-differentiated tariffs may attract consumers who benefit without responding to the price, simply because they have a favourable consumption pattern. It is thus important to understand on which basis consumers choose between tariffs. We model the choice as a function of compensating welfare measures, and use a discrete choice model on data from a residential dynamic pricing experiment. The results indicate that higher demand flexibility will tend to increase the propensity to select dynamic tariffs, while consumption patterns do not influence tariff choice significantly.     

Efficient pricing and investment in electricity markets with intermittent resources

Facing growing technological and environmental challenges, the electricity industry needs effective pricing mechanism to promote efficient risk management and investment decisions. In a restructured electricity market with competitive wholesale prices and traditionally regulated retail rates, however, there are technical and institutional barriers that prevent dynamic pricing with price responsive demand. In regions with limited energy storage capacity, intermittent renewable resources present special challenges. This could adversely affect the effectiveness of public policies causing inefficient investments in energy technologies. In this paper, we present an updated economic model of pricing and investment in restructured electricity market and use the model in a simulation study for an initial assessment of renewable energy strategy and alternative pricing mechanisms. A key objective of the study is to shed light on the policy issues so that effective decisions can be made to improve efficiency.     

On the efficient market diffusion of intermittent renewable energies

Capacity costs of renewable energies have been decreasing dramatically and are expected to fall further, making them more competitive with fossils. Building on an analytically tractable peak-load pricing model, we analyze how intermittency of renewable energies affects the market diffusion that results from these lower costs. In particular, once renewables have become competitive by attaining the same levelized cost of electricity (LCOE) as fossils, the marginal increase in efficient capacities due to a further cost reduction varies substantially. Initially it is small, then it rises, but it falls again once renewable capacities are large enough to satisfy the whole electricity demand at times of high availability. If external costs of fossils are internalized by a Pigouvian tax, then perfect competition leads to efficient investments in renewable and fossil capacities; even though we assume that only a subgroup of consumers can adapt their demand to price fluctuations that are caused by the intermittency of renewables. Moreover, fossils receive a capacity payment through the market for their reliability in serving demand of non-reactive consumers. Maximum electricity prices rise with the share of renewables. If regulators impose a price cap, this initially raises investments in renewables, but the effect may reverse if the share of renewables is large.     

Household energy use,energy efficiency,emissions, and behaviors

Energy and greenhouse gas (GHG) emissions generally aim to (i) reduce energy use and hence emissions, (ii) steer consumers away from fossil fuels and/or electricity generated from fossil fuels, and (iii) align demand and supply, making sure that the existing infrastructure can handle times of high demand. Policies thus include a variety of pricing schemes, taxes on energy inputs, energy efficiency standards and incentives, and renewables standards and incentives. Ex ante and ex post analyses of their effectiveness thus rely crucially on understanding how consumers respond to pricing schemes, taxes, and other policies. This paper presents an overview of the challenges faced when empirically estimating household energy demand. It describes the difficulties associated with estimating the price elasticity of demand, discussing behavioral responses that may make consumers relatively insensitive to price changes or taxes. It also surveys empirical evidence about non-price policies, such as clearer information or real-time feedback about energy use, and appeal to norms. The paper concludes discussing evidence about the rebound effect, the energy efficiency gap, and how suppliers respond to a variety of policies.     

Residential energy efficiency investment and behavioural response under different electricity pricing schemes: a physical-microeconomic approach

ABSTRACT

The purpose of this paper is to analyze the effects of electricity price changes and energy efficiency subsidy on household energy efficiency purchase and/or behavioural adjustment decisions. The analysis adds energy efficiency investment to a methodology that merges the physics of energy with microeconomic principles. The physical side informs the amount of electricity used to satisfy services that people desire, while the microeconomic side imposes a utility function that represents a household’s welfare. Several electricity pricing schemes and energy efficiency options are examined, with costs and benefits of each option explicitly modeled in the physical representation. Several insights are derived from performing an analysis for archetypical villas across Saudi Arabia. One, energy efficiency purchases lower the need for energy conservation. Households also lessen the extent to which they practice conservation as energy efficiency subsidies are raised. Additionally, as energy efficiency subsidies and electricity prices rise, the difference in household spending on other goods and services widens between the highest efficiency case and no added efficiency. This indirect rebound causes a situation where firms would increase their production, and thus energy use, to meet the additional demand by households for their goods.     

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.     

Composite forecasting approach,application for next-day electricity price forecasting

Accurate forecasting of electricity prices can provide significant benefits to energy suppliers when allocating their assets and to energy consumers for defining an optimal portfolio. There are numerous methods that efficiently support the forecasting of time series, such as electricity prices, which have high volatility. However, the performance of these approaches varies depending on data sets and operational conditions. In this work, the concept of composite forecasting is presented and implemented in a retrospective study, in real industrial forecasting conditions to show the potential of forecast performance improvement and comparable high consistency of a forecast performance across different ‘Day Peak’ and ‘Day Base’ electricity price data sets for different seasons. As individual methods support vector regression, artificial neural networks and ridge regression are implemented. The forecast performances of these methods are evaluated and compared with their forecast combination using different error measures. The results show that composite forecasting processes with ‘inverse root mean squared error’ combination approach can generate, on average, a more accurate and robust forecast than using an individual methods or other combination schemas.     

Regionally-varying and regionally-uniform electricity pricing policies compared across four usage categories

The objective of our research is to predict how electricity demand varies spatially between status quo regionally-uniform electricity pricing and hypothetical regionally-varying electricity pricing across usage categories. We summarize the empirical results of a case study of electricity demand in South Korea with three key findings and their related implications. First, the price elasticities of electricity demand differ across usage categories. Specifically, electricity demands for manufacturing and retail uses are price inelastic and close to unit elastic, respectively, while those for agricultural and residential uses are not statistically significant. This information is important in designing energy policy, because higher electricity prices could reduce electricity demands for manufacturing and retail uses, resulting in slower growth in those sectors. Second, spatial spillovers in electricity demand vary across uses. Understanding the spatial structure of electricity demand provides useful information to energy policy makers for anticipating changes in demand across regions via regionally-varying electricity pricing for different uses. Third, simulation results suggest that spatial variations among electricity demands by usage category under a regionally-varying electricity-pricing policy differ from those under a regionally-uniform electricity-pricing policy. Differences in spatial changes between the policies provide information for developing a realistic regionally-varying electricity-pricing policy according to usage category.     

The optimization of demand response programs in smart grids

The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques.     

A real options evaluation model for the diffusion prospects of new renewable power generation technologies

This study presents a policy planning model that integrates learning curve information on renewable power generation technologies into a dynamic programming formulation featuring real options analysis. The model recursively evaluates a set of investment alternatives on a year-by-year basis, thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of renewable power generation technologies. Price uncertainty is introduced through stochastic processes for the average wholesale price of electricity and for input fuel prices. Demand for electricity is assumed to be increasingly price-sensitive, as the electricity market deregulation proceeds, reflecting new options of consumers to react to electricity price changes (such as time-of-use pricing, unbundled electricity services, and choice of supplier). The empirical analysis is based on data for the Turkish electricity supply industry. Apart from general implications for policy-making, it provides some interesting insights about the impact of uncertainty and technical change on the diffusion of various emerging renewable energy technologies.     

Smart metering for residential energy efficiency: The use of community based social marketing for behavioural change and smart grid introduction

Community-based social marketing (CBSM) has shown to be very effective at inducing behavioural change due to its pragmatic approach. It has been found that nonintegrated intensive approaches towards changing individual's behaviour, such as education and economic self-interest are not successful.This paper will explain how a large urban electricity meter replacement program can achieve a reduction in peak demand and overall energy consumption through the use of advanced metering infrastructure (AMI or ‘smart meters’) coupled with CBSM, which in turn enables the progression towards a ‘smart grid’. In order to measure success the following targets were set:

• •Peak demand reduction (peak lopping) of 20% from the households participating in the Behaviour Change Programs (BCPs).
• •Peak demand shifting (load shifting) to reduce energy consumption during ‘super peak’ by 10% in BCP participating households.
• •Average total energy use reduction of 10% in BCP participating households.

The energy efficiency actions discussed with householders during eco-coaching, and other feedback communications, are identified by utilising the information regarding barriers and benefits generated from the research phase prior to coaching. These actions can include referral to other initiatives such as the provision of reduced cost solar PV power systems, direct load control devices for domestic air-conditioners, the time-of-use pricing product, the provision of in-home-displays (IHD) and other devices necessary for development of a ‘smart grid’.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey

China’s residential electricity demand has grown rapidly over the last three decades and given the expected continued growth, demand side management (DSM) can play an important role in reducing electricity demand. By using micro-level data collected from 1450 households in 27 provinces in the first-ever China Residential Energy Consumption Survey in 2012, this study estimates the effects of three DSM measures empirically: tiered household electricity pricing, China Energy Label program, and information feedback mechanisms. We find these measures have contributed to moderating residential electricity demand growth but additional policy reform and tools are needed to increase their effectiveness and impact. Residential electricity demand is found to be price- and income- inelastic and tiered pricing alone may not be as effective in electricity conservation. The statistically significant relationship between China Energy Label efficient refrigerators - but not televisions - and lowered residential electricity consumption reflect mixed program effectiveness. Lastly, of the information feedback currently available through electricity bills, payment frequency and meters, only meter reader is estimated to be statistically significant. Important policy implications and recommendations for improving each of these three DSM measures to expand their impact on reducing residential electricity consumption are identified.     

Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing

This paper studies the dynamic demand for residential electricity in Taiwan employing a monthly panel data set, composed of 19 counties and spanning the period from 2007:01 to 2013:12. The partial adjustment model used addresses the endogeneity of the electricity price that results from the increasing-block pricing. The estimated results show that there is a significant seasonal difference in the demand for electricity between the summer and non-summer periods. Both the adjustment speed and own price elasticity during the summer months are found to be lower than those in the non-summer months due to the hot weather in summer. It is easier for consumers to adjust their electricity consumption in response to the changes in electricity pricing during the non-summer time. The estimated inelastic short-run and long-run income effects show that electricity is a necessity for consumers. Moreover, the controversial electricity-conservation policies are found to be ineffective measures for reducing electricity consumption in Taiwan.     

A new approach to congestion pricing in electricity markets: Improving user pays pricing incentives

Electricity pricing has traditionally been based on average cost pricing where consumers pay a ‘flat’ tariff based upon the average cost of production and transportation of electricity. The introduction of new ‘smart’ meters allows electricity providers to differentiate tariffs on the basis of time. Utilising congestion pricing theory, the energy industry has embraced ‘time-of-use’ (ToU) tariffs with a view to more efficiently pricing electricity. This paper demonstrates that pricing as a function of demand variability (reflecting capacity utilisation) is a more appropriate alternative to existing ToU tariffs for more efficiently allocating costs to end users. We call this new alternative pricing model ‘first derivative ratio’ FDR pricing. This new approach to congestion pricing could be applied to markets other than electricity, such as road transportation.     

Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception

Increased demand response is essential to fully exploit the Swedish power system, which in turn is an absolute prerequisite for meeting political goals related to energy efficiency and climate change. Demand response programs are, nonetheless, still exceptional in the residential sector of the Swedish electricity market, one contributory factor being lack of knowledge about the extent of the potential gains. In light of these circumstances, this empirical study set out with the intention of estimating the scope of households' response to, and assessing customers' perception of, a demand-based time-of-use electricity distribution tariff. The results show that households as a whole have a fairly high opinion of the demand-based tariff and act on its intrinsic price signals by decreasing peak demand in peak periods and shifting electricity use from peak to off-peak periods.