scholarly journals Program Potential: Estimates of Federal Energy Cost Savings from Energy Efficient Procurement

2012 ◽  
Author(s):  
Margaret Taylor ◽  
K. Sydny Fujita
Keyword(s):  
Energy Cost ◽  
Energy Efficient ◽  
Cost Savings

Data-Driven Design of Control Strategies for Distributed Energy Systems

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Philip Odonkor ◽  
Kemper Lewis
Keyword(s):  
Energy Cost ◽  
Cost Minimization ◽  
Computational Cost ◽  
Cost Savings ◽  
Price Volatility ◽  
Energy Resources ◽  
Data Driven ◽  
Distributed Energy Resources ◽  
Maximization Problem ◽  
Distributed Energy

Abstract The flexibility afforded by distributed energy resources in terms of energy generation and storage has the potential to disrupt the way we currently access and manage electricity. But as the energy grid moves to fully embrace this technology, grid designers and operators are having to come to terms with managing its adverse effects, exhibited through electricity price volatility, caused in part by the intermittency of renewable energy. With this concern however comes interest in exploiting this price volatility using arbitrage—the buying and selling of electricity to profit from a price imbalance—for energy cost savings for consumers. To this end, this paper aims to maximize arbitrage value through the data-driven design of optimal operational strategies for distributed energy resources (DERs). Formulated as an arbitrage maximization problem using design optimization principles and solved using reinforcement learning, the proposed approach is applied toward shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building clusters, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies for energy cost minimization. The scalability of this approach is studied using two test cases, with results demonstrating an ability to scale with relatively minimal additional computational cost, and an ability to leverage system flexibility toward cost savings.

Designing Optimal Arbitrage Policies for Distributed Energy Systems in Building Clusters Using Reinforcement Learning

2019 ◽  
Author(s):  
Philip Odonkor ◽  
Kemper Lewis
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Electricity Markets ◽  
Energy Cost ◽  
Cost Minimization ◽  
Control Strategies ◽  
Cost Savings ◽  
Price Volatility ◽  
Maximization Problem ◽  
Distributed Energy

Abstract In the wake of increasing proliferation of renewable energy and distributed energy resources (DERs), grid designers and operators alike are faced with several emerging challenges in curbing allocative grid inefficiencies and maintaining operational stability. One such challenge relates to the increased price volatility within real-time electricity markets, a result of the inherent intermittency of renewable energy. With this challenge, however, comes heightened economic interest in exploiting the arbitrage potential of price volatility towards demand-side energy cost savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building cluster demand profiles, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies towards energy cost minimization. Finally, the approach is shown to be computationally tractable, designing efficient strategies in approximately 5 hours of training over a simulation time horizon of 1 month.

A Comparative Study of Industrial Energy Assessments for Small and Medium-Sized Industrial Facilities

2018 ◽  
Author(s):  
Ahmad I. Abbas ◽  
Mandana S. Saravani ◽  
Muhannad R. Al-Haddad ◽  
Ryoichi S. Amano ◽  
Mohammad Darwish Qandil
Keyword(s):  
Energy Cost ◽  
Analytical Study ◽  
Energy Savings ◽  
Cost Savings ◽  
Assessment Process ◽  
Industrial Facilities ◽  
Manufacturing Plants ◽  
Energy Assessment ◽  
University Of Wisconsin ◽  
Industrial Energy

The Industrial Assessment Center at University of Wisconsin-Milwaukee (WM-IAC) has implemented over 100 industrial energy, waste, and productivity assessments, and has recommended $9.5 million of energy and operational savings with about 950 recommendations since it was re-established in 2011. This paper analyzes the assessments, and the recommendations were performed over two years only, 2014 and 2015. During these two years, a total of 40 assessments were created by visiting different manufacturing facilities with the analysis of the data gathered and processed. The determinants of the data were the number of recommendations, recommended energy savings (in kWh/year), recommended energy cost savings (in US$/year), implemented energy savings (in US$/year), the Standard Industrial Code (SIC) and the groups of Energy Efficiency Opportunities (EEOs). Such an analytical study was meant to reveal the significance of EEO groups through a variety of SICs in terms of the potential for energy savings, particularly focused towards choosing plant facilities for IAC assessments. Additionally, this paper could be considered as a guide for plant managers, energy engineers and other personnel involved in the energy assessment process. Conclusions are inferred with respect to the most promising EEOs that can be resolved based on the characteristics of the manufacturing plants visited. The information investigated can pave the way for composing energy demanding industries and expose priority goal areas regarding minimizing the energy consumption.

Estimating the cost savings and avoided CO2 emissions in Brazil by implementing energy efficient policies

Energy Policy ◽  
2014 ◽  
Vol 67 ◽  
pp. 4-15 ◽  
Author(s):  
Rodrigo F. Calili ◽  
Reinaldo C. Souza ◽  
Alain Galli ◽  
Margaret Armstrong ◽  
André Luis M. Marcato
Keyword(s):  
Co2 Emissions ◽  
Energy Efficient ◽  
Cost Savings ◽  
The Cost

scholarly journals A generic method for energy-efficient and energy-cost-effective production at the unit process level

2016 ◽  
Vol 113 ◽  
pp. 508-522 ◽  
Author(s):  
Xu Gong ◽  
Toon De Pessemier ◽  
Wout Joseph ◽  
Luc Martens
Keyword(s):  
Energy Cost ◽  
Energy Efficient ◽  
Cost Effective ◽  
Unit Process ◽  
Process Level

Energy Cost Savings Through Direct Purchase and Transportation

Natural Gas ◽  
2007 ◽  
Vol 4 (2) ◽  
pp. 23-32
Author(s):  
Donald F. Santa
Keyword(s):  
Energy Cost ◽  
Cost Savings

A Simplified Method to Predict Energy Cost Savings in Office Buildings Using a Hybrid Desiccant, Absorption Chiller, and Natural Gas Turbine Cogeneration System With Thermal Storage

2007 ◽  
Author(s):  
James McNeill ◽  
Jon Previtali ◽  
Moncef Krarti
Keyword(s):  
Natural Gas ◽  
Gas Turbine ◽  
Energy Cost ◽  
Cost Savings ◽  
Thermal Storage ◽  
Analysis Tool ◽  
Conventional System ◽  
Absorption Chiller ◽  
Cogeneration System ◽  
Simplified Analysis

This paper provides a simplified analysis tool to predict the energy savings associated with the usage of a hybrid air conditioning system that combines liquid desiccant, absorption chiller, natural gas turbine cogeneration system with thermal storage (hereafter hybrid cogeneration system) versus a watercooled centrifugal chiller with a natural gas boiler (hereafter conventional system). The hybrid cogeneration system is controlled to track both electrical and thermal loads. The simplified analysis method is formulated from detailed energy simulation models. A direct correlation has been determined between the energy cost savings of using the hybrid cogeneration system instead of the conventional system and the cogeneration capacity, peak electricity rate, and natural gas rate for five U.S. cities: Atlanta, Chicago, Denver, New York, and San Francisco.

scholarly journals Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

2021 ◽  
Author(s):  
Xiaopu Peng ◽  
Tathagata Bhattacharya ◽  
Ting Cao ◽  
Jianzhou Mao ◽  
Taha Tekreeti ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Cost ◽  
Energy Efficient ◽  
Data Centers ◽  
Energy Resources ◽  
Energy Utilization ◽  
Stable Operation ◽  
Efficient Data ◽  
Renewable Energy Generation ◽  
Fluctuate Level

Abstract To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability of UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewableenergy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.

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