Present-day economic and environmental constraints push power systems to be operated closer to their limits. A common limiting factor for power transmission is the risk of voltage instability in recent years. As the ultimate countermeasure to voltage collapse, load shedding is normally considered the last resort, when there are no other alternatives to stop an approaching voltage collapse. The requirements of a practical load shedding scheme are to prevent a power system from voltage collapse and to maximize its reliability. In order to design such a scheme, the following tasks are equally important: 1. Recognizing the approaching voltage collapse. 2. Determining the best load shedding locations. 3. Minimizing the amount of load shedding. This thesis firstly investigates the widely used undervoltage load shedding schemes (UVLS) and the single-port impedance match (SPIM) based schemes. The findings explain the difficulties faced by them. An original load shedding oriented voltage stability monitoring scheme, which involves developing a new multi-port network equivalent, is then developed. With the help of the multi-port network equivalent, the monitoring scheme can not only recognize the approaching voltage collapse in time, but also can easily rank the load buses based on their weakness. The results of ranking are consistent with those obtained from modal analysis method. This thesis then proposes a practical event-driven load shedding scheme based on the experiences learned from the schemes implemented by various utilities. The scheme involves developing a multistage method, which is to optimize the amount of load shedding. A general design procedure for the scheme is presented in the thesis. Using a real 2038 bus system as an example, the design methodology is described in detail. The methodology is expected to help power system engineers develop their own load shedding schemes. A practical emergency demand response scheme is also developed and presented in the appendix. It is aimed at choosing the proper demand response participants and minimizing the total cost while achieving a certain level of operation reserves. / Power Engineering and Power Electronics

The Swedish electrical power system faces many challenges. Stricter environmental and economic demands require a more efficient use of both the transmission and distribution grids as well as the production capabilities. Since the Swedish national demand of electricity is fluctuating, the system has always been dimensioned to meet the periods of high demand, resulting in a low utilization of the system. To meet these challenges, the concept of a “Smart Grid” has been phrased. One of the most important goals of a Smart Grid is to enable end-consumers to participate more actively in the energy market. One way to do this is through “load-shifting” where consumption (or loads) are moved from hours of high demand (peak hours) to hours of low demand (off-peak hours). Load-shifting is a part of a set of intentional consumption modifications denoted “Demand Response” (DR) and is deemed to be one of the most important tools of the Smart Grid. In Sweden, a Smart Grid project called the Stockholm Royal Seaport (SRS) project is currently taking place. The project have phrased a hypotheses regarding load-shifting called the “Active customer” scenario, in which a customer load-shifts 5-15 % of his electricity consumption. To facilitate this scenario, the SRS project uses an end-consumer price model for electricity, called the SRS price model, as well as technological and market solutions not yet available on a national scale.   This study investigates what impact the results from the SRS pilot project might have if implemented for private apartment end-consumers on a Swedish national scale. The study is divided into three parts. The first part investigates the challenges of a national scale implementation of private apartment end-consumer DR and the SRS price model. The second part investigates what the impact would be if the entire Swedish private apartment end-consumer sector where to act in accordance with the Active customer scenario. The third part consists of a sensitivity analysis. Four challenges for a national private apartment end-consumer load-shift implementation have been elicited. They are; the lack of easily moveable loads in a foreseeable future, the heterogeneous cost of distribution, the suggested price models low peak to off-peak price ratio and the comparatively small cost of electricity of the private apartment end-consumers. The SRS price model is deemed to give a clear economic incentive for load-shift of private apartment end-consumer without electric heating. However, the incentive might be considered too weak with yearly savings of 48-165 SEK for a 15 % load-shift, depending on apartment consumption. This corresponds to yearly savings of 124 to 429 million SEK for the entire customer segment. These challenges are deemed to be of a non-technical character, but rather of a marketing and communication nature. The impact of a fully implemented national private apartment end-consumer load-shift in accordance with the Active customer scenario and the SRS price model is deemed to be beneficial from an overall power system point of view. However, the impact on the private apartment end-consumer national demand is small in comparison with other plausible system developments, such as energy demand reductions due to more efficient lighting solutions. The sensitivity analysis of private apartment end-consumer cost savings when acting in accordance with the Active customer scenario indicates that the percentage savings may increase in the future when considering more volatile prices for electric energy or the implementation of a time differentiated energy tax.

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

With the economic development, the high quality has become a critical issue for service continuous of power companies. To ensure the stable power supply, the asset management of power equipments is applied to prevent the system outage. With voluminous distribution transformers over very wide area, the real time monitoring of temperature has been included in the scope of smart grid. During recent years, the service outage due to transformer overloading has caused customer panic as well as deterioration of service quality. This thesis develops the Transformer Terminal Unit (TTU) by integration of computer chip for power consumption, DSP and sampling circuit of temperature measurement to achieve the functions of real time monitoring of transformer operation condition. When an abnormal operation condition such as overloading or high oil temperature occurs, the TTU can report the contingency back to the control station via the hybrid communication system so that the distribution system operators can take remedy action to prevent the contingency. The actual loading and temperature of transforms are also measured and collected in this study to develop the relationship of temperature and loading levels. By collecting transformer temperature, the power demand of a transformer can be estimated and the load shedding can then be activated to prevent the problem of overloading when the temperature exceeds the operation constraint.

Power converters are used in many distributed energy resources (DER) applications. With proper controls, DER systems can reduce losses and achieve higher energy efficiency if various power sources and loads are integrated through DC bus. High voltage electric vehicle (EV) DC charging station is becoming popular in order to reduce charging time and improve energy efficiency. A DC EV charging station model involving photovoltaic, energy storage system (ESS), fuel cell and DC loads is studied in this work. A dynamic programming technique that considers various uncertainties involved in the system is adopted to obtain optimal dispatch of ESS and fuel cell system. The effects of different tariffs, demand response programs and contract capacities of demand in the power scheduling are investigated and the results are presented.

The main topic of this research is the efficient operation of a modernized distribution grid from both the customer side and utility side. For the customer side, this dissertation discusses the planning and operation of a customer with multiple demand response programs, energy storage systems and distributed generators; for the utility side, this dissertation addresses the implementation and assessment of voltage/VAR control and conservation voltage reduction in a distribution grid with distributed generators. The objectives of this research are as follows: (1) to develop methods to assist customers to select appropriate demand response programs considering the integration of energy storage systems and DGs, and perform corresponding energy management including dispatches of loads, energy storage systems, and DGs; (2) to develop stochastic voltage/VAR control techniques for distribution grids with renewable DGs; (3) to develop optimization and validation methods for the planning of integration of renewable DGs to assist the implementation of voltage/VAR control; and (4) to develop techniques to assess load-reduction effects of voltage/VAR control and conservation voltage reduction. In this dissertation, a two-stage co-optimization method for the planning and energy management of a customer with demand response programs is proposed. The first level is to optimally select suitable demand response programs to join and integrate batteries, and the second level is to schedule the dispatches of loads, batteries and fossil-fired backup generators. The proposed method considers various demand response programs, demand scenarios and customer types. It can provide guidance to a customer to make the most beneficial decisions in an electricity market with multiple demand response programs. For the implementation of voltage/VAR control, this dissertation proposes a stochastic rolling horizon optimization-based method to conduct optimal dispatches of voltage/VAR control devices such as on-load tap changers and capacitor banks. The uncertainties of renewable DG output are taken into account by the stochastic formulation and the generated scenarios. The exponential load models are applied to capture the load behaviors of various types of customers. A new method to simultaneously consider the integration of DGs and the implementation of voltage/VAR control is also developed. The proposed method includes both solution and validation stages. The planning problem is formulated as a bi-level stochastic program. The solution stage is based on sample average approximation (SAA), and the validation stage is based on multiple replication procedure (MRP) to test the robustness of the sample average approximation solutions of the stochastic program. This research applies big data-driven analytics and load modeling techniques to propose two novel methodologies to assess the load-reduction effects of conservation voltage reduction. The proposed methods can be used to assist utilities to select preferable feeders to implement conservation voltage reduction.

This report asks how Austin Energy can optimally operate residential energy efficiency and demand side management programs including demand response measures. Efficient energy use is the act of using less energy to provide the same level of service. Demand side management encompasses utility initiatives that modify the level and pattern of electrical use by customers, without adjusting consumer behavior. Demand side management is required when a utility must respond to increasing energy needs, or demand, by its customers. In order to achieve the 20% carbon emissions and 800 MW peak demand reductions mandate of the Generation, Resource and Climate Plan, AE must aggressively pursue an increase in customer participation by expanding education and technical services, enlist the full functionality of a smart grid and subsequently reduce energy consumption, peak demand, and greenhouse gas emissions. Energy efficiency is in fact the cheapest source of energy that Austin Energy has at its disposal between 2010 and 2020. But this service threatens Austin Energy’s revenues. With the ascent of onsite renewable energy generation and advanced demand side management, utilities must address the ways they generate revenues. As greenhouse gas emissions regulations lurk on the horizon, the century-old business model of “spinning meters” will be fundamentally challenged nationally in the coming years. Austin Energy can develop robust analytical methods to determine its most cost-effective energy efficiency options, while creating a clear policy direction of promoting energy efficiency while addressing the three-fold challenges of peak demand, greenhouse gas emissions and total energy savings. This report concludes by providing market-transforming recommendations for Austin Energy. / text

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Based on a previous empirical study of the effect of a residential demand response program in Sala, Sweden, this project  investigated the economic consequences of consumer behaviour change after a demand-based time of use distribution tariff was employed. The economic consequences of consumers were proven to be disadvantageous in terms of unit electricity price. Consumers could achieve more electricity bill saving through stabilising their electricity consumption during peak hours, and this way bring least compromising of their comfort level. In order to estimate the price elasticity of the studies demand response program, a new method of estimation price elasticity was proposed. With this method, the intensity of demand response of the demand response program was estimated in terms of price elasticity. Regression analysis was also applied to find out the price incentives of consumer behaviour change. And the results indicated that the rise in electricity supply charge hardly contributes to load reduction, while the demand-based tariff constituted an advantageous solution on load demand management. However stronger demand response still requires better communication with customers and more incentives other than the rise in distribution tariff.

Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the existing power system, which cannot cope effectively with highly variable and distributed energy resources. The emergence of smart grid technologies in recent year has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This thesis investigates the impact of smart grid technologies on the integration of wind power into the power system. A smart grid power system model is developed and validated by comparison with a real-life smart grid experiment: the Olympic Peninsula Demonstration Experiment. The smart grid system model is then expanded to include 1000 houses and a generic generation mix of nuclear, hydro, coal, gas and oil based generators. The effect of super-imposing varying levels of wind penetration are then investigated in conjunction with a market model whereby suppliers and demanders bid into a Real-Time Pricing (RTP) electricity market. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. It is also found that the degree to which Greenhouse Gas (GHG) emissions can be mitigated is highly dependent on the generation mix. A displacement of natural gas based generation during peak demand can for instance lead to an increase in GHG emissions. Of practical significance to power system operators, the simulations also demonstrate that Demand Response (DR) can reduce generator cycling and improve generator efficiency, thus potentially lowering GHG emissions while also reducing wear and tear on generating equipment. / Graduate