Because of shortage of natural resources, Taiwan must rely on the imported fossil fuels such as coal and petroleum for power generation. The demand of fossil fuel for all over the world causes increasing energy cost and global warming. Thus, to execute energy-saving policies and to reduce the amount of carbon producing can help many countries to decrease the amount of energy usage and global warming. This thesis proposes a intelligent digital meter by integrating a energy metering IC, microprocessor, and RS485 which support demand response to control loads for residential and commercial customers. The master station can execute real-time management to measure different power consumption, by each load device to support the analysis of customer consumption, and load forecasting with RS485. From the result, the developed intelligent digital meter to verify residential or commercial energy-saving effect and potential.

Due to lack of natural resources, a lot of fossil fuels such as coal and petroleum has to be imported for power generation. Both energy demand all over the world and the price of energy have been increased. To slow down the speed of global warming and to reduce the amount of carbon producing, various energy-saving policies have been applied by many counties to reduce the amount of energy usage. This thesis proposes a energy management system by integrating a ZigBee-based remote controller and a multi-function digital meter for residential and commercial customers. It can be easily installed without requesting modification of original circuits of electric appliances to achieve load for the demand response of the power company. The master station reads the power consumption of appliance through the RS-232 interface and detects controls the state of the remote device. With VB software, the system can measure the amount of power usage, by each load device to support the analysis of customer consumption. The load reduction can easily be achieved with the remote control according to the demand response issued by utility control center. By measuring and verifying the energy management system, the performance of power management is improved.

The shift towards more renewable energy sources is imminent, this shift is accelerated by the technological advancement and the rise of environmental awareness. However, this shift causes major operational problems to the current grid that is optimised for unidirectional power flow. Besides the operational problems, there are problems related to the optimal tariff scheme. In this thesis a study on the effect of the adoption of photovoltaic solar panels and the electric vehicles on the households' electricity demand profile is presented. The change on the demand profile is going to affect the current tariffs, this effect is also explored in this thesis. In this thesis real life data on household electricity use and photovoltaic power production was used. For electric vehicle charging simulated data was used. Besides that, a demand response scheme for electric vehicle is proposed in order to estimate the savings potential of this demand response on the electricity bill. The results show that the change in the demand profile is not merely a change in the total energy consumption, but it is a change in the power peaks as well. The peaks change significantly in condominiums and rental apartments, in this households' type it increases by around 80%, while in detached and row houses little change is noticed on the peaks, yet they still increase by around 10%. The demand response shows around 1- 12% savings in the distribution bill depending on the household, however it showed more incentives for condominiums and rental apartments. The current distribution tariffs perform asymmetrically with the various households. However, one tariff ensures 11.7 MSEK financial revenue for the distribution system operator, this is higher than the other tariffs' revenue by more than 28.5%. The new prospective situation requires totally different tariffs that ensure a balance between firstly a reasonable revenue for the distribution system operator and secondly incentives for consumers to self produce electricity as well as to reduce their peaks.

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The purpose of this study is to identify realistic opportunities and barriers regarding PEV charge management by analyzing real-world PEV data from customers in the Austin Energy service area and evaluating direct, quantifiable economic value benefits as it relates new revenue, cost avoidance, CO2 reductions, and MW potential for peak shaving. The main objective is to provide business analysis to support the strategic road-map for Austin Energy PEV home charging programs. Three main charge program implementations are considered: Uncontrolled Charging, Time of Use Rates, and One Way Utility Control. The data used for the analysis includes 45 households with PEVs from Mueller area; 24 were under a Time of Use trial with pricing incentives to charge at night, and 21 receive normal Austin Energy rates. Data analysis shows that 66% of Time of Use trial group successfully shifted PEV load to Off Peak hours (10:00PM to 6:00AM). The potential of One Way control, based on load availability for interruption, shows that it will not be possible to implement until there are 37,000 PEVs in the Austin Energy area. Uncontrolled Charging represents a risk by increasing load during the residential peak. Time of Use Rates program will incentivize load shifting, reduce wholesale energy costs for Austin Energy while allowing customers to reduce their overall electricity bill. / text

In this thesis, a demand elasticity model is developed and tested for the dispatch of high voltage power systems and microgrids. The price obtained from dispatching a network in a base-case scenario is used as input to a price-elastic demand model. This demand model is then used to determine the price-responsive demand for the next iteration, assuming that the load schedule is defined a day-ahead. Using this scheme, trends for demand, hourly prices, and total operation costs for a system can be obtained to study the impact of demand response on unit commitment and dispatch of distributed energy resources. This way, the effect on the scheduling of dispatchable generators and energy storage systems can be analyzed with respect to price-elastic loads. The results for a test power system and a benchmark microgrid show that as the demand is more elastic, the longer it takes for the dispatch to converge to a final condition. The 24-hour model eventually converges to a steady state, with prices and costs at their lowest values for different scenarios, which is good for most system participants and desirable in a market environment, thus highlighting the importance of price-responsive loads in electricity markets.

The development of future smart electricity grids is driven by efficiency and climate targets and economic benefit for producers, retailers and customers on the deregulated electricity market. Since most investments will be made by grid owners acting as regulated monopolies, it is unclear how they will get return on their investments. Can demand response programs create cost reductions for the grid owner that help motivate the investment in smart grids? Two cases of cost reduction opportunities are evaluated assuming that peak loads are reduced by a demand response program: optimization of cable dimensions for lower peak loads when building new grids, and avoided investments in reinforced capacity in the existing grid. Potential cost reductions are estimated for the two example cases, using financial and technical data for Fortum's local distribution grid in Stockholm. The result shows that reducing the capacity in the cables by 70-80 % only brings down investment costs by 3-4 %, since the common expense for excavation outweighs the incremental cost of cables. Over-capacity means increased redundancy and flexibility to increase load in the future, which are valuable features for a grid owner.Regarding investments in the existing grid, a substation that needs replacement because of overload is analyzed. Assuming a continued trend of steadily increasing load, a 34 % peak load reduction would delay the investment 20 years, which is in turn worth 900,000 SEK in 2010 prices.

New possibilities are developing in the infrastructure of electrical systems to meet the new demand of more volatile power generation. This study focuses on German household reactions to price changes and their economic and renewable utilization effects.   In order to model the effects of flexible prices in the Freiamt region, the basic research – including interviews and data collecting – is carried out in the fields of economics and renewable energy. An elasticity model based on the Spees and Lave study in used to simulate consumer behaviour to changing prices.   Two pricing structures with daily and hourly changing prices are found to lower the average electrical prices in both cases. These benefits are larger overall with the hourly price changes when all other variables are kept constant. This study finds that the changes to load patterns also seem to correlate with the local renewable energy production. Results suggest that this specific form of energy generation benefits from consumer reactions to changing prices during 2007 and 2008.   In order to validate these results the model should be expanded to include a more differentiated load from different sectors and to include a wider range of the electrical prices advertised to the consumer. However, under given circumstances, this study concludes that using more renewable power generation is possible both generally with daily price changes and also more specifically with hourly changing prices at a more competitive market price.

This Master’s Report outlines graduate research work completed by Akshay Sriprasad, who is supervised by Professor Tom Edgar, in the area of modeling and systems optimization for the smart grid. The scope this report includes the development and validation of strategies to elicit demand response, defined as reduction of peak demand, at the residential level, in conjunction with collaborative research efforts from the Pecan Street Research Institute, a smart grid research consortium based in Austin, TX. The first project outlined is an artificial neural network-­‐based demand forecasting model, initially developed for UT’s campus cooling system and adapted for residential homes. Utilizing this forecasting model, a number of demand response-­‐focused optimization studies are carried out, including optimization of community energy storage for peak shifting, and electric vehicle charging optimization to harness inexpensive night-­‐time Texas wind energy. Community energy storage and electric vehicles are chosen as ideal dynamic charging media due to increased proliferation and focus of Pecan Street Research Institute on critical emerging technologies. As these two technologies involve significant capital investment, an alternative mobile application-­‐based demand response strategy is outlined to complete a comprehensive portfolio of demand response strategies to suit a variety of budgets and capabilities. / text

In this report some possible consequences of introducing demand response in the electric power grid are studied. Demand response is a part of the Smart Grid, which is a technology being developed to use our electric power grids more efficiently. Demand response programs aim to move people’s power usage over different times of the day, for example to distribute the power usage more evenly throughout the day or to permit a larger share of renewable, intermittent power sources in the system without making the delivery of electric power less stable.  A distribution system operator (DSO) can encourage customers to shift their power usage between different hours by various tariffs, for example by using time-differentiated or power dependent tariffs.   In this thesis, the change in power losses and possible environmental impact of introducing due to a power shift is studied. Power input curves from a DSO, Sala-Heby Energi AB, are studied and modified to simulate a power shift with an evened out electric power usage. The studies made show that in the best-case scenario, that is a electric power usage evened out to 100% each day, the power losses in the whole grid can be reduced with 2.6%. The environmental study shows that the result varies greatly with what method is chosen to do the calculations. The results are presented in kg CO2-equivalents (CO2e), and depending on method used they can either decrease or increase. The environmental study show that the environmental impact from the power usage is more dependent on the shift in power usage between hours than the decrease in electric power losses.