scholarly journals Optimal Regulation of Variable Speed Pumps in Sewer Systems

2020 ◽  
Vol 2 (1) ◽  
pp. 58
Author(s):  
Luigi Cimorelli ◽  
Oreste Fecarotta
Keyword(s):  
Energy Cost ◽  
Hybrid Genetic Algorithm ◽  
Cost Savings ◽  
General Rule ◽  
Mixed Integer ◽  
Variable Speed ◽  
Optimal Regulation ◽  
Sewer Systems ◽  
Derivative Free ◽  
Programming Package

In this work, the optimal regulation of variable speed pump (VSP) was solved by means of two optimization algorithms: a mixed-integer optimizer based on the BONMIN (Basic Open-Source Nonlinear Mixed Integer Programming) package, and an original hybrid genetic algorithm (GA) called GA–Powell’s direction set method (PDSM), which employs a derivative free inner optimizer, that is, the Powell’s direction set method (PDSM). The obtained results show how the use of a strategy based on the optimal regulation of VSP allows to obtain huge energy cost savings. The analysis of the results shows that the regulation of the plant does not apparently follow a general rule.

scholarly journals Optimal Regulation of Pumping Station in Water Distribution Networks Using Constant and Variable Speed Pumps: A Technical and Economical Comparison

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2530 ◽  
Author(s):  
Luigi Cimorelli ◽  
Carmine Covelli ◽  
Bruno Molino ◽  
Domenico Pianese
Keyword(s):  
Distribution System ◽  
Energy Demand ◽  
Water Distribution ◽  
Energy Savings ◽  
Greenhouse Gas Emission ◽  
Distribution Networks ◽  
Variable Speed ◽  
Variable Speed Drives ◽  
Optimal Regulation ◽  
Energy Consuming

Greenhouse gas emission is one of the main environmental issues of today, and energy savings in all industries contribute to reducing energy demand, implying, in turn, less carbon emissions into the atmosphere. In this framework, water pumping systems are one of the most energy-consuming activities. The optimal regulation of pumping systems with the use of variable speed drives is gaining the attention of designers and managing authorities. However, optimal management and operation of pumping systems is often performed, employing variable speed drives without considering if the energy savings are enough to justify their purchasing and installation costs. In this paper, the authors compare two optimal pump scheduling techniques, optimal regulation of constant speed pumps by an optimal ON/OFF sequence and optimal regulation with a variable speed pump. Much of the attention is devoted to the analysis of the costs involved in a hypothetical managing authority for the water distribution system in order to determine whether the savings in operating costs is enough to justify the employment of variable speed drives.

Generalized Field-Development Optimization With Derivative-Free Procedures

SPE Journal ◽  
2014 ◽  
Vol 19 (05) ◽  
pp. 891-908 ◽  
Author(s):  
Obiajulu J. Isebor ◽  
David Echeverría Ciaurri ◽  
Louis J. Durlofsky
Keyword(s):  
Computational Cost ◽  
Optimization Method ◽  
Optimal Number ◽  
Global Search ◽  
Search Method ◽  
Pattern Search ◽  
Mixed Integer ◽  
Categorical Variables ◽  
Field Development ◽  
Derivative Free

Summary The optimization of general oilfield development problems is considered. Techniques are presented to simultaneously determine the optimal number and type of new wells, the sequence in which they should be drilled, and their corresponding locations and (time-varying) controls. The optimization is posed as a mixed-integer nonlinear programming (MINLP) problem and involves categorical, integer-valued, and real-valued variables. The formulation handles bound, linear, and nonlinear constraints, with the latter treated with filter-based techniques. Noninvasive derivative-free approaches are applied for the optimizations. Methods considered include branch and bound (B&B), a rigorous global-search procedure that requires the relaxation of the categorical variables; mesh adaptive direct search (MADS), a local pattern-search method; particle swarm optimization (PSO), a heuristic global-search method; and a PSO-MADS hybrid. Four example cases involving channelized-reservoir models are presented. The recently developed PSO-MADS hybrid is shown to consistently outperform the standalone MADS and PSO procedures. In the two cases in which B&B is applied, the heuristic PSO-MADS approach is shown to give comparable solutions but at a much lower computational cost. This is significant because B&B provides a systematic search in the categorical variables. We conclude that, although it is demanding in terms of computation, the methodology presented here, with PSO-MADS as the core optimization method, appears to be applicable for realistic reservoir development and management.

Integration of Predated Notifications of Personal Actions for HR-Planning in ERP-Systems

2012 ◽  
pp. 291-303 ◽  
Author(s):  
Peter Cissek ◽  
Jorge Marx Gomez
Keyword(s):  
Human Resource ◽  
Business Processes ◽  
Planning Process ◽  
Cost Savings ◽  
General Rule ◽  
Resource Planning ◽  
Erp Systems ◽  
Fully Integrated ◽  
Related Data ◽  
The Difference

This chapter intends to reveal the benefit of predated notifications of personal actions for HR-planning and discusses the interrelated demands on ERP-systems. If e-government is implemented, one has to think of rearranging the government’s HR-structure in order to adapt to the new circumstances, too. This means to take advantage of modern HR-methodology in order to become more efficient in HR-administration. One possible way in improving human resource management (HRM) is using predated notifications of personal actions for HR-planning. Human resource planning (HR-planning) is a component of strategic enterprise planning. It is fully integrated into the enterprise-wide planning process, because HR-planning is not only determined by other planning areas, but it also determines them vice versa. So the more precisely and comprehensively HR-planning is done, the more accurate derived key figures, which are used in other planning areas, can be. Governments usually deal with a huge amount of personnel, so HR is one of the main tasks in administration. Predated notifications of personal actions usually are known in present, but will be started in the future. In contrast to planning a personnel action the predated one will take place with the highest possible probability. An example for making the difference more clear may be an employee’s retirement. It does not stringently depend on the employee’s age, but rather on the person’s individual decision to retire. As a general rule, an employee’s intention to retire is already known about half a year before it takes place. If this information is used in the planning process, the company will have enough time to estimate the loss of knowledge or the cost-savings that will be caused by the employee’s withdrawal. In huge companies, HRM typically is supported by ERP-systems. The functionality offered by the software depends on the company’s needs and may range from a simple keeping of personnel data to a complex module called human capital management, which is used for payroll accounting, talent management, employee self services, and many more. If the decision-making body considers the company’s personnel as business critical, a lot of employee-related data is collected and analyzed, ranging from master data to planning key figures. This chapter will emphasize the importance of efficient HR-planning for governments in order to improve their business processes. It can be seen as one of the goals of e-government. It will be pointed out how HR-planning can be improved by using predated notifications of personal actions, so that HR-divisions in governments can use advanced HR-planning right on from the beginning when preparing themselves for e-government.

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.

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