Next-Generation Performance-Based Regulation. Volume 1: Introduction - Global Lessons for Success; Volume 2: Primer - Essential Elements of Design and Implementation; Volume 3: Innovative Examples from Around the World
Jeffrey LoganOwen ZinamanDavid LittellCamille KadochPhil BakerRanjit BharvirkarMax DupuyBrenda HausauerCarl LinvillJanine Migden-OstranderJan RosenowXuan Wang
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Performance-based regulations (PBRs) provide a regulatory framework to connect goals, targets, and measures to utility performance or executive compensation. Well-designed PBRs provide incentives for utility performance, benefiting consumers and utility owners alike. This report considers the role of both PBRs and more discrete performance incentive mechanisms (PIMs) in 21st century power sector transformation. Innovative technologies are transforming the way electricity is generated, delivered, and consumed. PBRs have the potential to realign utility, investor, and consumer incentives and mitigate emerging challenges to the utility business model, renewable integration, and even cyber security.The goals of PBRs in the form of multi-year rate plans are in many respects the same in terms of providing reasonably priced and reliable service to customers. However, today's technologies have changed, and there is more emphasis on clean energy. Thus, the pathways and the potential outcomes are different than they were in the 20th century when centralized generator stations and large infrastructure additions dominated the utility landscape. Given unprecedented changes underway in the electricity sector, PBRs - by specifying expectations of utility performance and outcomes for consumers, while staying agnostic to the exact means of delivery - constitute a form of prescient regulation that harnesses disruption. PBRs are one tool in a broader toolbox in the transition toward flexible regulatory and market structures that rewards utilities that adapt or evolve in reaction to market and technology change. PBRs and PIMs have great value for the electric industry when designed well and can be applied to many different situations. How exactly PBR mechanisms are most effectively enacted will vary based on the utility ownership model, institutional arrangements, and a variety of other local factors. PBRs should be tailored to the needs and goals of each jurisdiction, and perhaps each utility, to most effectively achieve the needs of a 21st century power grid in that jurisdiction. Presented in three volumes, this report highlights the lessons learned from their evolving history, explores essential elements of their design and implementation as well as considerations for how they may be best applied, and examines leading examples of PBRs from the United Kingdom, New York, Denmark, Mexico, and South Africa. The full report, 'Next-Generation Performance Based Regulation - Emphasizing Utility Performance to Unleash Power Sector Innovation,' published in September 2017, can be accessed at https://www.nrel.gov/docs/fy17osti/68512.pdf.Keywords:
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Electric Utility
Three main goals have been assigned to Dutch electricity policy: a reliable, affordable and clean electricity sector. In order to make the electricity section cleaner by decreasing GHG emissions, the SDE+ regulation has been introduced to increase the share of renewable energies in the overall electricity production. The drawback of the introduction of renewable energies is that it also makes to the electricity grid less reliable. The main reason for this increased unreliability is the incapacity of the electricity network to cope with high electricity production fluctuations. One solution to cope with high production fluctuations is to allow electricity consumers to respond to variations of electricity prices resulting from the availability of electricity on the network over time. To allow demand response, households may purchase smart grid appliances. These appliances, for example smart washing machines or smart fridges, adapt their electricity consumption depending on the price of electricity. The benefits for households is that the purchase of smart gird appliances may lead to lower electricity costs. There is however a large uncertainty about the extent to which households will be willing to purchase smart grid appliances and provide demand response. Since demand response is important to allow a wider integration of renewable energies in the Dutch electricity sector, policy measures to support adoption of smart grid appliances might be needed. To create effective policies, more insight must be gained into the factors leading to a high share of smart grid appliance adoption. The purpose of this work is to identify a set of directions for policy that allows a large adoption of smart grid appliances on the scale of city districts. The identification of directions for policy is done by using an agent-based model. This model allows simulating the decision-making performed by each individual households to decide whether to purchase smart grid appliances. In addition, it enables to incorporate the effects of interactions between households on the final adoption percentage of smart grid appliances. The analysis in this work has been performed by using the Diffusion of Innovations Theory of Rogers (1962). In this theory, (potential) adopters are assumed to be divided among five different categories: innovators, early adopters, early majority, late majority and laggards. Adopters of each category have different reasons to adopt and different roles in the diffusion of innovations. The outcomes of the model shows that the adoption of smart grid appliances by innovators and early adopters, which typically represents a small size of the population, open to change and having sufficient financial resources, is not expected to be difficult. Encouraging the formation of consumer groups, for example of the scale of city streets, and the nomination of product ambassadors may largely contribute to the adoption of smart grid appliances by households belonging to these adopter categories. Underlining the benefits of adoption for the environment and for community building is expected to offer important motivation. Model outcomes however also underline the fact that convincing early majority population, late majority population and laggards, which typically represents a large size of the population, might be extremely difficult in the case of smart grid appliances. As explained by Rogers, adopters belonging to these populations are pragmatics. Important factors influencing adoption are the amount of savings that can be made through the purchase of smart grid appliances and the easiness of their utilisation. The large-scale adoption of smart grid appliances would therefore require that the plug-and-play concept is applied to smart grid appliances. According to several studies, most households request a large amount of savings per month in order to spend more on smart grid appliances. The amount of savings that can be made in comparison to these expected is low. Therefore, the expectation is that smart grid appliances will only be adopted on large scale in case their purchase costs equals the ones of traditional appliances. The adoption of smart grid appliances does however not necessarily mean that they will be used in their ‘smart function’. Hence, the large-scale adoption of smart gird appliances does not necessarily mean that a city district is able to shift strongly its electricity demand when needed. The support of adoption by early majority population, late majority population and laggards on the scale of city districts is not expected to be effective, since the challenge mostly lies in the technological and commercial adjustment of smart grid appliances. Only when the characteristics of smart grid appliances will be interesting enough for the population categories just cited, will it become effective again to support adoption on the scale of city districts. The challenge will then become to promote a new electricity consumption behaviour. This new electricity consumption behaviour is only expected to be accepted if the changes in consumption behaviour are minimal.
Electricity retailing
Demand Response
Consumption
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This study outlines a methodology for assessing changes in household electricity consumption and CO2 emissions. The method is proposed to analyse large datasets of residential electricity consumption in the case study of the first smart metering pilot project in Latvia. The goal of the project is to achieve a reduction of electricity consumption in households by 10%. In order to do so project aims to increase household user's awareness of smart technologies, as well as to promote households' involvement in energy efficiency measures. The proposed methodology is based on several steps, including: 1) baseline situation analysis; 2) normalisation of electricity consumption data; 3) identification of the factors affecting household electricity consumption; 4) an empirical analysis of households' electricity consumption using regression analysis; 5) assessment of changes in electricity consumption at the end of evaluation period and 6) calculation of CO2 emissions. Finally, the first results of the smart metering pilot project in nine-month period have been presented. The recommendations for policy development on promoting smart metering have been raised at the end of this paper.
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Over the last decade there has been an increased focus on changing domestic electricity consumption behaviors. While the usual approach has been to facilitate reduced consumption, recent work has started looking at facilitating more flexible electricity use as a means of shifting consumption to more favorable times. This approach means that people may behave more sustainably without necessarily using less electricity. Exploring this emerging approach, this paper presents a study of flexibility in domestic electricity use as facilitated by an eco-feedback system with forecast information about price, availability of green energy, and grid demand. The prototype system was deployed in three households for 22 weeks. Our findings show that flexible electricity use is far from trivial to achieve in domestic households. The details of this is relevant for understanding people's ability and willingness to shift electricity consumption, and for the design of systems that facilitate doing this.
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Electricity system
Electricity retailing
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This study outlines a methodology for assessing changes in household electricity consumption and CO2 emissions. The method is proposed to analyse large datasets of residential electricity consumption in the case study of the first smart metering pilot project in Latvia. The goal of the project is to achieve a reduction of electricity consumption in households by 10%. In order to do so project aims to increase household user's awareness of smart technologies, as well as to promote households' involvement in energy efficiency measures. The proposed methodology is based on several steps, including: 1) baseline situation analysis; 2) normalisation of electricity consumption data; 3) identification of the factors affecting household electricity consumption; 4) an empirical analysis of households' electricity consumption using regression analysis; 5) assessment of changes in electricity consumption at the end of evaluation period and 6) calculation of CO2 emissions. Finally, the first results of the smart metering pilot project in nine-month period have been presented. The recommendations for policy development on promoting smart metering have been raised at the end of this paper.
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Electricity consumption increases substantially over the years where residential use significantly contributes to the overall consumption. The growth in the population and variety of home appliances together with increasing comfort levels of the people results in higher levels of residential electricity use. In fact, nearly one fourth of Turkey's total electricity consumption is due to the domestic use. To achieve global sustainability targets and reduce the overall electricity use, focusing on the domestic consumption is crucial. In this research, the energy consumptions patterns of households are determined to identify the potential electricity savings existing in the residential sector. Moreover, specific policy recommendations which can promote the behavioral change are driven by measuring the responsiveness of people to different measures and the combinations of these measures such as information, feedback, rewards, and social influences. A survey was conducted to determine the patterns and the responsiveness of the residential customers. The results obtained from the survey are used to depict a general view of Turkish households towards electricity consumption behaviors and their energy efficiency attitudes. Responses indicate there should be more regulations and improvements in energy policy. An electricity allocation problem is solved in order to see possible impacts of behavioral change measures on the network. Scenarios are defined for each policy and allocation problem is solved to see the possible generation cost reduction. Also, gas emissions for each scenario is recorded to understand the possible effects of policies on the environment. Results show that behavioral change studies seem to be well worth to study. In order to reach residential efficiency, possible policy alternatives are suggested for Turkish households.
Consumption
Rebound Effect
Electricity retailing
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Increased renewable electricity production, coupled with emerging sectors of electricity consumption such as electric vehicles, has led to the desire to shift the times of the day electricity is consumed to better match generation. Different methods have been proposed to shift residential electricity use from the less desirable times to more desirable times, including: feedback technology, pricing incentives, smart appliances, and energy storage. Based on our experience in this area, we present three challenges for residential shifting: getting users to understand the concept of shifting, determining when to shift and communicating that to users, and accounting for the dynamic nature of shifting. We argue that encouraging residential electricity shifting is much more challenging than electricity curtailment, and suggest an increased focus on understanding the everyday practices of users, which are crucial in order to shift electricity use.
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Electricity retailing
Load shifting
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Analyses of conservation technologies have typically concentrated on consumer investment criteria. This has had the effect of seriously limiting the ability to compare conservation measures directly to energy supply options. A methodology is demonstrated that allows comparison of the cost effectiveness of conservation measures from three different perspectives: a private consumer's perspective (with and without policy incentives); a utility's short-run perspective; and a utility's long-run perspective. Nineteen residential electrical conservation measures currently available were analyzed. From the consumer's perspective, the cost of conserving energy was compared with the price the consumer is expected to pay for electricity over the next 10 years. For the utility's short-run perspective, the cost of conserving energy was compared to the fuel cost of generating that electricity (taking into account time of day and season of energy savings). In the utility's long-run perspective, the cost of conserving energy was compared with the cost of electricity from various existing and new generation facilities. Pacific Gas and Electric Company (PG and E) was the utility analyzed.
Electric Utility
Mains electricity
Investment
Consumption
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Consumption
Mains electricity
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This chapter contains sections titled: Understanding Toolbox Administering Toolbox Configuring Toolbox Alternatives to Toolbox Summary
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This paper describes a software toolbox (a software library) designed for condition monitoring and diagnosis of machines. This toolbox implements both new methods and prior art and is aimed for practical down-to-earth data analysis work. The target is to improve knowledge of the operation and behaviour of machines and processes throughout their entire life-cycles. The toolbox supports different phases of condition based maintenance with tools that extract essential information and automate data processing. The paper discusses principles that have guided toolbox design and the implemented toolbox structure. Case examples are used to illustrate how condition monitoring applications can be built using the toolbox. In the first case study the toolbox is applied to fault detection of industrial centrifuges based on measured electrical current. The second case study outlines an application for centralized monitoring of a fleet of machines that supports organizational learning.
Toolbox
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