Energy management : DOE actions to improve oversight of contractors' subcontracting practices : report to the Chairman, Committee on Governmental Affairs, U.S. Senate /

"B-245444"--P. 1.

A heat metering system for energy management on an industrial site

Faced with a future of rising energy costs there is a need for industry to manage energy more carefully in order to meet its economic objectives. A problem besetting the growth of energy conservation in the UK is that a large proportion of energy consumption is used in a low intensive manner in organisations where they would be responsibility for energy efficiency is spread over a large number of personnel who each see only small energy costs. In relation to this problem in the non-energy intensive industrial sector, an application of an energy management technique known as monitoring and targeting (M & T) has been installed at the Whetstone site of the General Electric Company Limited in an attempt to prove it as a means for motivating line management and personnel to save energy. The objective energy saving for which the M & T was devised is very specific. During early energy conservation work at the site there had been a change from continuous to intermittent heating but the maintenance of the strategy was receiving a poor level of commitment from line management and performance was some 5% - 10% less than expected. The M & T is concerned therefore with heat for space heating for which a heat metering system was required. Metering of the site high pressure hot water system posed technical difficulties and expenditure was also limited. This led to a ‘tin-house' design being installed for a price less than the commercial equivalent. The timespan of work to achieve an operational heat metering system was 3 years which meant that energy saving results from the scheme were not observed during the study. If successful the replication potential is the larger non energy intensive sites from which some 30 PT savings could be expected in the UK.

Redes elétricas inteligentes: desenvolvimento de modelos computacionais e aplicações para a gestão do lado da procura

Esta tese incide sobre o desenvolvimento de modelos computacionais e de aplicações para a gestão do lado da procura, no âmbito das redes elétricas inteligentes. É estudado o desempenho dos intervenientes da rede elétrica inteligente, sendo apresentado um modelo do produtor-consumidor doméstico. O problema de despacho económico considerando previsão de produção e consumo de energia obtidos a partir de redes neuronais artificiais é apresentado. São estudados os modelos existentes no âmbito dos programas de resposta à procura e é desenvolvida uma ferramenta computacional baseada no algoritmo de fuzzy-clustering subtrativo. São analisados perfis de consumo e modos de operação, incluindo uma breve análise da introdução do veículo elétrico e de contingências na rede de energia elétrica. São apresentadas aplicações para a gestão de energia dos consumidores no âmbito do projeto piloto InovGrid. São desenvolvidos sistemas de automação para, aquisição monitorização, controlo e supervisão do consumo a partir de dados fornecidos pelos contadores inteligente que permitem a incorporação das ações dos consumidores na gestão do consumo de energia elétrica; SMART GRIDS - COMPUTATIONAL MODELS DEVELOPMENT AND DEMAND SIDE MANAGMENT APPLICATIONS Abstract: This thesis focuses on the development of computational models and its applications on the demand side management within the smart grid scope. The performance of the electrical network players is studied and a domestic prosumer model is presented. The economic dispatch problem considering the production forecast and the energy consumption obtained from artificial neural networks is also presented. The existing demand response models are studied and a computational tool based on the fuzzy subtractive clustering algorithm is developed. Energy consumption profiles and operational modes are analyzed, including a brief analysis of the electrical vehicle and contingencies on the electrical network. Consumer energy management applications within the scope of InovGrid pilot project are presented. Computational systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters allowing to incorporate consumer actions on their electrical energy management.

Implementation and Validation of a Self-Consumption Maximization Energy Management Strategy in a Vanadium Redox Flow BIPV Demonstrator

This paper presents the results of the implementation of a self-consumption maximization strategy tested in a real-scale Vanadium Redox Flow Battery (VRFB) (5 kW, 60 kWh) and Building Integrated Photovoltaics (BIPV) demonstrator (6.74 kWp). The tested energy management strategy aims to maximize the consumption of energy generated by a BIPV system through the usage of a battery. Whenever possible, the residual load is either stored in the battery to be used later or is supplied by the energy stored previously. The strategy was tested over seven days in a real-scale VRF battery to assess the validity of this battery to implement BIPV-focused energy management strategies. The results show that it was possible to obtain a self-consumption ratio of 100.0%, and that 75.6% of the energy consumed was provided by PV power. The VRFB was able to perform the strategy, although it was noticed that the available power (either to charge or discharge) varied with the state of charge.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed.

Design and Testing of a Control System for Supply Side Electrical Load Management

India's energy demand is increasing rapidly with the intensive growth of economy. The electricity demand in India exceeded the availability, both in terms of base load energy and peak availability. The efficient use of energy source and its conversion and utilizations are the viable alternatives available to the utilities or industry. There are essentially two approaches to electrical energy management. First at the supply / utility end (Supply Side Management or SSM) and the other at the consumer end (Demand Side Management or DSM). This work is based on Supply Side Management (SSM) protocol and consists of design, fabrication and testing of a control device that will be able to automatically regulate the power flow to an individual consumer's premise. This control device can monitor the overuse of electricity (above the connected load or contracted demand) by the individual consumers. The present project work specially emphasizes on contract demand of every consumer and tries to reduce the use beyond the contract demand. This control unit design includes both software and hardware work and designed for 0.5 kW contract demand. The device is tested in laboratory and reveals its potential use in the field.

Heterogeneous Collaborative Sensor Network for Electrical Management of an Automated House with PV Energy

In this paper we present a heterogeneous collaborative sensor network for electrical management in the residential sector. Improving demand-side management is very important in distributed energy generation applications. Sensing and control are the foundations of the “Smart Grid” which is the future of large-scale energy management. The system presented in this paper has been developed on a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead-acid batteries, controllable appliances and smart metering. Therefore, there is a large number of energy variables to be monitored that allow us to precisely manage the energy performance of the house by means of collaborative sensors. The experimental results, performed on a real house, demonstrate the feasibility of the proposed collaborative system to reduce the consumption of electrical power and to increase energy efficiency.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Aggregating energy supply and demand

Energy usage in general, and electricity usage in particular, are major concerns internationally due to the increased cost of providing energy supplies and the environmental impacts of electricity generation using carbon-based fuels. If a "systems" approach is taken to understanding energy issues then both supply and demand need to be considered holistically. This paper examines two research projects in the energy area with IT tools as key deliverables, one examining supply issues and the other studying demand side issues. The supply side project used hard engineering methods to build the models and software, while the demand side project used a social science approach. While the projects are distinct, there was an overlap in personnel. Comparing the knowledge extraction, model building, implementation and interface issues of these two deliverables identifies both interesting contrasts and commonalities.

Modeling dynamic demand response using Monte Carlo simulation and interval mathematics for boundary estimation

With the rapid development of various technologies and applications in smart grid implementation, demand response has attracted growing research interests because of its potentials in enhancing power grid reliability with reduced system operation costs. This paper presents a new demand response model with elastic economic dispatch in a locational marginal pricing market. It models system economic dispatch as a feedback control process, and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response. Compared with the conventional “one time use” static load dispatch model, this dynamic feedback demand response model may adjust the load to a desired level in a finite number of time steps and a proof of convergence is provided. In addition, Monte Carlo simulation and boundary calculation using interval mathematics are applied for describing uncertainty of end-user's response to an independent system operator's expected dispatch. A numerical analysis based on the modified Pennsylvania-Jersey-Maryland power pool five-bus system is introduced for simulation and the results verify the effectiveness of the proposed model. System operators may use the proposed model to obtain insights in demand response processes for their decision-making regarding system load levels and operation conditions.

A reassessment of energy and GDP relationship: The case of Australia

This paper investigates the long- and short-run relationships between energy consumption and economic growth in Australia using the bound testing and the ARDL approach. The analytical framework utilized in this paper includes both production and demand side models and a unified model comprising both production and demand side variables. The energy-GDP relationships are investigated at aggregate as well as several disaggregated energy categories, such as coal, oil, gas and electricity. The possibilities of one or more structural break(s) in the data series are examined by applying the recent advances in techniques. We find that the results of the cointegration tests could be affected by the structural break(s) in the data. It is, therefore, crucial to incorporate the information on structural break(s) in the subsequent modelling and inferences. Moreover, neither the production side nor the demand side framework alone can provide sufficient information to draw an ultimate conclusion on the cointegration and causal direction between energy and output. When alternative frameworks and structural break(s) in time series are explored properly, strong evidence of a bidirectional relationship between energy and output can be observed. The finding is true at both the aggregate and the disaggregate levels of energy consumption.

Cost-effectiveness of electricity energy efficiency programs

We analyze the cost-effectiveness of electric utility ratepayer-funded programs to promote demand-side management (DSM) and energy efficiency (EE) investments. We specify a model that relates electricity demand to previous EE DSM spending, energy prices, income, weather, and other demand factors. In contrast to previous studies, we allow EE DSM spending to have a potential longterm demand effect and explicitly address possible endogeneity in spending. We find that current period EE DSM expenditures reduce electricity demand and that this effect persists for a number of years. Our findings suggest that ratepayer funded DSM expenditures between 1992 and 2006 produced a central estimate of 0.9 percent savings in electricity consumption over that time period and a 1.8 percent savings over all years. These energy savings came at an expected average cost to utilities of roughly 5 cents per kWh saved when future savings are discounted at a 5 percent rate. Copyright © 2012 by the IAEE. All rights reserved.

Energy efficiency policies: A retrospective examination

We review literature on several types of energy efficiency policies: appliance standards, financial incentive programs, information and voluntary programs, and management of government energy use. For each, we provide a brief synopsis of the relevant programs, along with available existing estimates of energy savings, costs, and cost-effectiveness at a national level. The literature examining these estimates points to potential issues in determining the energy savings and costs, but recent evidence suggests that techniques for measuring both have improved. Taken together, the literature identifies up to four quads of energy savings annually from these programs - at least half of which is attributable to appliance standards and utility-based demand-side management, with possible additional energy savings from the U.S. Department of Energy's (DOE's) ENERGY STAR, Climate Challenge, and Section 1605b voluntary programs to reduce carbon dioxide (CO 2) emissions. Related reductions in CO 2 and criteria air pollutants may contribute an additional 10% to the value of energy savings above the price of energy itself. Copyright © 2006 by Annual Reviews. All rights reserved.