Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Presents an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. Part of a series of research reports that examines energy issues facing California

South Carolina utility demand-side management & system and pricing overview 2009 : a report

The South Carolina Energy Conservation and Efficiency Act of 1992 requires all utilities to report their demand-side activities. The intent of the legislation was to encourage the implementation of additional DSM activities. The objective of this report is to summarize the DSM activities of those utilities that contributed such information and to place these activities in context by providing a basic system and pricing overview

Saving energy, saving money : overview of demand-side management by South Carolina electric and natural gas utilities, 2010

Demand-side management is a strategy that electric and natural gas utilities employ to decrease or defer demand for their energy services. DSM in South Carolina hit a high-water mark in 2010 as an increasing number of utilities implemented measures to control peak energy demand and reduce the growth of overall energy demand. The following report is intended to inform utility customers, consumer advocates, state and local policymakers, and energy market professionals about the DSM programs implemented by South Carolina’s electric and natural gas utilities in 2010

South Carolina utility demand-side management & system and pricing overview 2008 : a report

The South Carolina Energy Conservation and Efficiency Act of 1992 requires all utilities to report their demand-side activities.The intent of the legislation was to encourage the implementation of additional DSM activities. The objective of this report is to summarize the DSM activities of those utilities that contributed such information and to place these activities in context by providing a basic system and pricing overview

Reliability Constrained Optimal Investment in a Microgrid with Renewable Energy, Storage, and Smart Resource Management

Environmental concerns have led to a rapid increase in renewable energy development and production as the global demand for electricity continues to increase. The intermittent and uncertain nature of electricity generation from renewable sources, such as wind and solar, however, create significant challenges in maintaining power system reliability at reasonable costs. Energy storage and smart-grid technologies are perceived to provide potential solutions to these challenges in modern power systems of different sizes. This work investigates the opportunity to incorporate energy storage in microgrids with renewable energy production, as well as applying smart microgrid management techniques to reduce the lifetime costs while maintaining an acceptable level of reliability. A microgrid consisting of a 5 home community with generation supplied by two propane generators to meet the “N-1” reliability criterion is used as the base case scenario. Actual load data of typical homes is obtained from the industry partner. An equivalent loss of load expectation criterion is used to benchmark the acceptable reliability level. A model is developed to calculate the lifetime operational cost of the base case scenario which is used to assess the benefit of the addition of renewable energy sources, energy storage, and smart microgrid management techniques. A MATLAB program is developed to assess the 20 year operational costs of various combinations of renewable energy sources and battery energy storage, which will be considered the lifetime of the system. The combination of generation and storage which yields the lowest lifetime operational cost is defined as the optimized microgrid, and is used as a basis to determine if additional savings are realized by the implementation of a microgrid operated by a Smart Microgrid Management System (SMMS). The conceptual layout of the proposed SMMS is presented along with identified methods of utilizing in-home thermal storage. The SMMS mechanism is discussed along with proposed functionality, potential methods of employment, and associated development and implementation costs. The microgrid operated by the SMMS is assessed, and its lifetime operational cost is presented and contrasted against the base case microgrid and the optimized microgrid. A power system reliability evaluation of the proposed microgrids are conducted using a probabilistic method to ensure that reliability is not sacrificed by the implementation of a cost-minimized microgrid. A sequential Monte Carlo simulation model is developed to assess the power system reliability of the various microgrid configuration cases. The functionality of this model is verified using an existing reliability assessment program. The results from the presented studies show that the implementation of renewable energy sources, energy storage, and smart microgrid management techniques are an effective way of reducing the operational cost of a remote microgrid while increasing its power system reliability.

End-use load control for power system dynamic stability enhancement

Faced with the prospect of increasing utilization of the transmission and distribution infrastructure without significant upgrade, the domestic electric power utility industry is investing heavily in technologies to improve network dynamic performance through a program loosely referred to as Flexible AC Transmission System (FACTS). Devices exploiting recent advances in power electronics are being installed in the power system to offset the need to construct new transmission lines. These devices collectively represent investment potential of several billion dollars over the next decade. A similar development, designed to curtail the peak loads and thus defer new transmission, distribution, and generation investment, falls under a category of technologies referred to as demand side management (DSM). A subset of broader conservation measures, DSM acts directly on the load to reduce peak consumption. DSM techniques include direct load control, in which a utility has the ability to curtail specific loads as conditions warrant. A novel approach has been conceived by Pacific Northwest National Laboratory (PNNL) to combine the objectives of FACTS and the technologies inherent in DSM to provide a distributed power system dynamic controller. This technology has the potential to dramatically offset major investments in FACTS devices by using direct load control to achieve dynamic stability objectives. The potential value of distributed versus centralized grid modulation has been examined by simulating the western power grid under extreme loading conditions. In these simulations, a scenario is analyzed in which active grid stabilization enables power imports into the southern California region to be increased several hundred megawatts beyond present limitations. Modeling results show distributed load control is up to 30 percent more effective than traditional centralized control schemes in achieving grid stability

Application of demand response programs for peak reduction using load aggregator

O aumento do consumo de energia requer atenção. Os especialistas propuseram muitas soluções para otimizar o uso de energia e propõem um sistema de gestão de energia eficiente. No entanto, desenvolver um sistema de energia que contempla agregadores de carga é óbvio para aprimorar o processo de gestão de energia. Este trabalho discute um sistema de gestão de energia para implementar programas de Demand Response (DR) usando abordagens de agregação de carga. Neste trabalho, dois estudos de caso comparam as diferentes respostas do sistema. O objetivo principal é discutir o papel de diferentes modelos de agregador de carga no sistema de energia, implementando um programa de DR. Esses agregadores de carga controlam diferentes tipos de cargas. Neste contexto, vários tipos de cargas domésticas são consideradas cargas controláveis. No processo de agregação, o objetivo é agregar as cargas que possuem as mesmas características usando a análise de agrupamento das cargas. A contribuição científica desta dissertação está relacionada com a redução da ponta e a agregação de cargas, considerando as cargas controláveis e os recursos de geração no sistema. Para atingir o objetivo anterior, foram realizados dois estudos de caso. Cada estudo de caso consiste em três cenários baseados no modelo de agregação de carga. Os resultados dos estudos indicam as respostas do sistema aos diferentes cenários e ilustram os méritos do modelo de agregador de carga. Além disso, os resultados demonstram como o agrupamento dos dispositivos de carga no sistema pode efetivamente fornecer redução de pico com recurso a programas de DR.The increment of energy consumption takes a high level of attention. The experts have proposed many solutions to optimize energy use and propose an efficient energy management system. However, verifying the load aggregators' role energy system is obvious to enhance the energy management process. This work discusses an energy management system to implement DR programs using load aggregation approaches. In this work, two case studies compare the different responses of the system. The main goal is to discuss the role of different load aggregator models in the power system by implementing a DR program. Those load aggregators control different types of loads. In this context, various types of domestic loads are considered controllable loads. In the aggregation process, the goal is to aggregate the loads that have the same features using the clustering analysis of the loads. The scientific contribution of this thesis is related to the integration of providing the peak reduction and the clustered aggregation of loads, considering the controllable loads and generation resources in the system. To achieve the previous goal, two case studies have been done. Each case study consists of three scenarios based on the load aggregation model. The results of the case studies indicate the system responses to the different scenarios and illustrate the merits of the load aggregator model. Furthermore, the results demonstrate how clustering the load appliances in the system can effectively provide peak reduction due to the DR programs