Because of the shortage of domestic energy resources in Taiwan, more than 97% of the energy has to be imported. The energy price has been increased dramatically during recent years due to the limited supply of conventional primary fossil energy resources. With the economic development and upgrade of people living standard, the electricity power consumption is increased significantly. To solve the problem, different strategies of energy conservation and CO2 emission reduction have been promoted by government to reduce that the peak loading growth and achieve better usage of electricity with more effective load management. This thesis proposes a digital smart meter which integrates the energy metering IC, microprocessor and hybrid communication schemes (Power Line Carrier/ZigBee/RS-485). The load control module and communication module are included in the smart meter to support various application functions. The embedded power management system (PMS) is also proposed to integrate with the smart meter to perform the demand response according to the real-time pricing and load management for residential and commercial customers. The master station can supervise the real-time power consumption of various load components to analyze the power consumption model of customers served and execute the demand load control. The actual demonstration system of embedded PMS has been set up to verify the function of energy management so that the customers have better understanding of power consumption by each appliance. In the future, the implementation of intelligent load control with an emergency load shedding of capability can help utility companies to achieve virtual power generation to enhance the power systems reliability. The customers may also reduce the electricity charge by executing demand response function, which disconnects the electricity service for non essential loads for either system emergency or high electricity peak pricing

Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Anil Pahwa / Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs and environmentally friendly operation. However, the anticipated increase of EV usage and increased use of renewable energy sources and smart storage devices for EV charging presents opportunities as well as challenges. Time-varying electricity pricing and day-ahead power commitment adds another dimension to this problem. This thesis, describes development of coordinated EV charging strategies for renewable energy-powered charging stations at homes and parking lots. We develop an optimal control theory-based charging strategy that minimizes power drawn from the electricity grid while utilizing maximum energy from renewable energy sources. Specifically, we derive a centralized iterative control approach in which charging rates of EVs are optimized one at a time. We also propose an algorithm that maximizes profits for parking lot operators by advantageously utilizing time-varying electricity pricing while satisfying system constraints. We propose a linear programming-based strategy for EV charging, and we specifically derive a centralized linear program that minimizes charging costs for parking lot operators while satisfying customer demand in available time. Then we model EV charging behavior of Active Consumers. We develop a real-time pricing scheme that results in favorable load profile for electric utility by influencing EV charging behavior of Active Consumers. We develop this pricing scheme as a game between electric utility and Active Consumers, in which the electric utilities decide optimal electricity prices that minimize peak-to-average load ratio and Active Consumers decide optimal charging strategy that minimizes EV charging costs for Active Consumers.

Yes / In the UK there is no real time retail market, and hence no real time retail electricity pricing. Therefore domestic electricity consumers in the UK pay electricity prices that do not vary from hour to hour, but are rather some kind of average price. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. The key question is whether we can evaluate energy management and renewable energy intervention in the behaviour of customers in real market terms. Currently only behaviour changes with respect to total consumption can be evaluated. Interventions cannot be defined for peak load behaviour. The effectiveness of the introduction of renewable energy is also hard to assess. Therefore, it is hard to justify introducing of renewable and demand side management at local community level, apart from when following government approved schemes, subsidies, and other initiatives. In this paper, a new criteria has been developed to help developers and planners of local residential communities to understand the cost of intervention, in order to evaluate where the load is when the prices are high.

Large penetration of distributed generation from variable renewable energy sources, increased consumption flexibility on the demand side and the electrification of transportation pose great challenges to existing and future electric distribution networks. This thesis studies the roles of several actors involved in electric distribution systems through electricity consumption data analysis and simulation models. Results show that real-time electricity pricing adoption in the residential sector offers economic benefits for end consumers. This occurs even without the adoption of demand-side management strategies, while real-time pricing also brings new opportunities for increasing consumption flexibility. This flexibility will play a critical role in the electrification of transportation, where scheduled charging will be required to allow large penetration of EVs without compromising the network's reliability and to minimize upgrades on the existing grid. All these issues add significant complexity to the existing infrastructure and conventional passive components are no longer sufficient to guarantee safe and reliable network operation. Active distribution networks are therefore required, and consequently robust and flexible modelling and simulation computational tools are needed for their optimal design and control. The modelling approach presented in this thesis offers a viable solution by using an equation-based object-oriented language that allows developing open source network component models that can be shared and used unambiguously across different simulation environments.

Traditionally, most consumers of electricity pay for their consumption according to a fixed-rate. The few existing implementations of real time pricing have been restricted to large industrial consumers, where the benefits could justify the high implementation cost. With the advancement of Smart Grid technologies, large scale implementation of variable-rate metering will be more practical. Consumers will be able to control their electricity consumption in an automated fashion, where one possible scheme is to have each individual maximize their own utility as a noncooperative game. In this thesis, noncooperative games are formulated among the consumers of Smart Grid with two real-time pricing schemes, where the Nash equilibrium operation points are investigated for their uniqueness and load balancing properties. The first pricing scheme charges a price according to the average cost of electricity borne by the retailer and the second charges according to a time-variant increasing-block price. The zero revenue model and the constant revenue rate model, are the two revenue models being considered. The relationship between these games and certain congestion games, known as atomic flow games from the computer networking community, is demonstrated. It is shown that the proposed noncooperative game formulation falls under the class of atomic splittable flow games. It is shown that the Nash equilibrium exists for four different cases, with different pricing schemes and revenue models, and is shown to be unique for three of the cases, under certain conditions. It is shown that both pricing schemes lead to similar electricity loading patterns when consumers are interested only in the minimization of electricity costs. Finally, the conditions under which the increasing-block pricing scheme is preferred over the average cost based pricing scheme are discussed.

No description available.

Paper [I] Using a unique and highly detailed data set on energy consumption at the appliance-level for 200 Swedish households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e.g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from "expensive" to "cheap" hours, using the Nord Pool spot prices as a proxy for a dynamic price, are computed to be very small; roughly 2-4% reduction in total daily costs from shifting load up to five hours ahead, indicating small incentives for households (and retailers) to adopt dynamic pricing of electricity. Paper [II] Using a detailed data set on appliance-level electricity consumption at the hourly level, we provide the first estimates of hourly and end-use-specific income elasticities for electricity. Such estimates are informative about how consumption patterns in general, and peak demand in particular, will develop as households’ income changes. We find that the income elasticities are highest during peak hours for kitchen and lighting, with point estimates of roughly 0.4, but insignificant for space heating. Paper [III] In this paper, I estimate the price elasticity of electricity as a function of the choice between fixed-price and variable-price contracts. Further, assuming that households have imperfect information about electricity prices and usage, I explore how media coverage of electricity prices affects electricity demand, both by augmenting price responsiveness and as a direct effect of media coverage on electricity demand, independent of prices. I also address the endogeneity of the choice of electricity contract. The parameters in the model are estimated using unique and detailed Swedish panel data on monthly household-level electricity consumption. I find that price elasticities range between −0.025 and −0.07 at the mean level of media coverage, depending on contract choice, and that households with monthly variation in electricity prices respond more to prices when there is extensive media coverage of electricity prices. When media coverage is high, for example 840 news articles per month (which corresponds to the mean plus two standard deviations), the price elasticity is −0.12, or 1.7 times the elasticity at the mean media coverage. Similarly, media coverage is also found to have a direct effect on electricity demand. Paper [IV] I explore how households switch between fixed-price and variable-price electricity contracts in response to variations in price and temperature, conditional on previous contract choice. Using panel data with roughly 54000 Swedish households, a dynamic probit model is estimated. The results suggest that the choice of contract exhibits substantial state dependence, with an estimated marginal effect of previous contractchoiceof0.96, andthattheeffectofvariationinpricesandtemperatureonthechoice of electricity contract is small. Further, the state dependence and price responsiveness are similar across housing types, income levels and other dimensions. A plausible explanation of these results is that transaction costs are larger than the relatively small cost savings from switching between contracts.

This thesis comprises three papers examining the impact of competitive pricing or competition on participants in energy markets. The scope of each paper is narrow but focused, dealing with one particular aspect of competition in each market under study. It is hoped that results from these three studies could provide valuable policy lessons to public policy makers in their task to create or maintain competition in different energy markets, so as to improve efficiencies in these markets. The first and second papers examine the load shifting behavior of industrial customers in Ontario under real time pricing (RTP). Using Hourly Ontario Energy Price (HOEP) data from 2005 to 2008 and industry-level consumption data from all industrial customers directly connected to the transmission grid, the first paper adopts a Generalized Leontief specification to obtain elasticities of substitution estimates for various industry groups, while the second paper adopts a specification derived from standard consumer theory to obtain price elasticity estimates. The findings of both papers confirm that in some industries, industrial customers who are direct participants of the wholesale market tend to shift consumption from peak to off-peak periods in order to take advantage of lower off-peak prices. Furthermore, in the first paper, a demand model is estimated and there is evidence that the marginal effect of hourly load on hourly price during peak periods is larger than the marginal effect during off-peak periods. An important policy implication from the results of these papers is that while RTP is currently limited to industrial customers, it does have positive spillover effects on all consumers. The third paper uses a unique panel dataset of all retail gasoline stations across five Canadian cities from late-2006 to mid-2007 to examine the effect of local competition on market shares and sales of individual stations. The base empirical specification includes explanatory variables representing the number of same brand stations and the number of different brand stations within a 3km radius to identify brand affiliation effect. It is found that the number of local competitors is negatively correlated with market share and sales. More interestingly, a same brand competitor has a larger marginal impact on market share and sales than a competitor of a different brand. These findings suggest that additional local competition leads to cannibalization of market share among existing stations, rather than create new demand. Another implication is that relying only on the number of different brands operating within a geographic market could understate the competition intensity in the local market.

This thesis comprises three papers examining the impact of competitive pricing or competition on participants in energy markets. The scope of each paper is narrow but focused, dealing with one particular aspect of competition in each market under study. It is hoped that results from these three studies could provide valuable policy lessons to public policy makers in their task to create or maintain competition in different energy markets, so as to improve efficiencies in these markets. The first and second papers examine the load shifting behavior of industrial customers in Ontario under real time pricing (RTP). Using Hourly Ontario Energy Price (HOEP) data from 2005 to 2008 and industry-level consumption data from all industrial customers directly connected to the transmission grid, the first paper adopts a Generalized Leontief specification to obtain elasticities of substitution estimates for various industry groups, while the second paper adopts a specification derived from standard consumer theory to obtain price elasticity estimates. The findings of both papers confirm that in some industries, industrial customers who are direct participants of the wholesale market tend to shift consumption from peak to off-peak periods in order to take advantage of lower off-peak prices. Furthermore, in the first paper, a demand model is estimated and there is evidence that the marginal effect of hourly load on hourly price during peak periods is larger than the marginal effect during off-peak periods. An important policy implication from the results of these papers is that while RTP is currently limited to industrial customers, it does have positive spillover effects on all consumers. The third paper uses a unique panel dataset of all retail gasoline stations across five Canadian cities from late-2006 to mid-2007 to examine the effect of local competition on market shares and sales of individual stations. The base empirical specification includes explanatory variables representing the number of same brand stations and the number of different brand stations within a 3km radius to identify brand affiliation effect. It is found that the number of local competitors is negatively correlated with market share and sales. More interestingly, a same brand competitor has a larger marginal impact on market share and sales than a competitor of a different brand. These findings suggest that additional local competition leads to cannibalization of market share among existing stations, rather than create new demand. Another implication is that relying only on the number of different brands operating within a geographic market could understate the competition intensity in the local market.

<div> <div> <div> <p>Real-time pricing (RTP) of electricity for consumers has long been argued to be crucial for realizing the many envisioned benefits of demand flexibility in a smart grid. However, many details of how to actually implement a RTP scheme are still under debate. Since most of the organized wholesale electricity markets in the US implement a two-settlement mechanism, with day-ahead electricity price forecasts guiding financial and physical transactions in the next day and real-time ex post prices settling any real-time imbalances, it is a natural idea to let consumers respond to the day-ahead prices in real-time. However, if such an idea is not controlled properly, the inherent closed-loop operation may lead consumers to all respond in the same fashion, causing large swings of real-time demand and prices, which may jeopardize system stability and increase consumers’ financial risks. </p><p><br></p> <p>To overcome the potential uncertainties and undesired demand peak caused by “selfish” behaviors by individual consumers under RTP, in this research, we develop a fully decentralized price-driven demand response (DR) approach under game- theoretical frameworks. In game theory, agents usually make decisions based on their belief about competitors’ states, which needs to maintain a large amount of knowledge and thus can be intractable and implausible for a large population. Instead, we propose using regret-based learning in games by focusing on each agent’s own history and utility received. We study two learning mechanisms: bandit learning with incomplete information feedback, and low regret learning with full information feedback. With the learning in games, we establish performance guarantees for each individual agent (i.e., regret minimization) and the overall system (i.e., bounds on price of anarchy).</p><p><br></p></div></div></div><div><div><div> <p>In addition to the game-theoretical framework for price-driven demand response, we also apply such a framework for peer-to-peer energy trading auctions. The market- based approach can better incentivize the development of distributed energy resources (DERs) on demand side. However, the complexity of double-sided auctions in an energy market and agents’ bounded rationality may invalidate many well-established theories in auction design, and consequently, hinder market development. To address these issues, we propose an automated bidding framework based on multi-armed bandit learning through repeated auctions, and is aimed to minimize each bidder’s cumulative regret. We also use such a framework to compare market outcomes of three different auction designs. </p> </div> </div> </div>