Power-saving policies for annual energy cost savings in green computing

In recent years, environmental and energy conservation issues have taken the central theme in the global business arena. The reality of rising energy cost and their impact on international affairs coupled with the different kinds of environmental issues has shifted the social and economic consciousness of the business community. “Greening” the computing equipment is a low-risk way of doing business. It not only helps the environment but also reduce costs. It is also one of the largest growing trends in business today. Hence, the business community is now in search of an eco-friendly business model. This chapter highlights the concept of green computing, green business, and their needs in the current global scenario.

Download Full-text

Data-Driven Design of Control Strategies for Distributed Energy Systems

Journal of Mechanical Design ◽

10.1115/1.4044077 ◽

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.

Download Full-text

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

Volume 2A: 45th Design Automation Conference ◽

10.1115/detc2019-97190 ◽

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.

Download Full-text

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

ASME 2018 12th International Conference on Energy Sustainability ◽

10.1115/es2018-7550 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text