scholarly journals A Stochastic Approach to Energy Cost Minimization in Smart-Grid-Enabled Data Center Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Abolfazl Ghassemi ◽  
Pejman Goudarzi ◽  
Mohammad R. Mirsarraf ◽  
T. Aaron Gulliver
Keyword(s):  
Smart Grid ◽  
Data Center ◽  
Energy Cost ◽  
Cost Minimization ◽  
Average Power ◽  
Cost Savings ◽  
Stochastic Approach ◽  
Green Energy ◽  
Data Center Network ◽  
Power Cost

We propose a Lyapunov drift-plus-penalty- (LDPP-) based algorithm to optimize the average power cost for a data center network. In particular, we develop an algorithm to minimize the operational cost using real-time electricity pricing with the integration of green energy resources from the smart grid. The LDPP technique can achieve significant energy cost savings under quality of service (QoS) constraints. Numerical results are presented to evaluate and validate our solution. These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.

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.

scholarly journals Energy cost minimization with job security guarantee in Internet data center

2017 ◽  
Vol 73 ◽  
pp. 63-78 ◽  
Author(s):  
Zhongjin Li ◽  
Jidong Ge ◽  
Chuanyi Li ◽  
Hongji Yang ◽  
Haiyang Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Energy Cost ◽  
Cost Minimization ◽  
Job Security ◽  
Internet Data Center

scholarly journals Energy Cost Minimization in Heterogeneous Cellular Networks with Hybrid Energy Supplies

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Bang Wang ◽  
Qiao Kong ◽  
Qiang Yang
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Minimization Problem ◽  
Energy Cost ◽  
Cost Minimization ◽  
Energy Allocation ◽  
Green Energy ◽  
User Association ◽  
Heterogeneous Cellular Networks ◽  
Cost Minimization Problem

The ever increasing data demand has led to the significant increase of energy consumption in cellular mobile networks. Recent advancements in heterogeneous cellular networks and green energy supplied base stations provide promising solutions for cellular communications industry. In this article, we first review the motivations and challenges as well as approaches to address the energy cost minimization problem for such green heterogeneous networks. Owing to the diversities of mobile traffic and renewable energy, the energy cost minimization problem involves both temporal and spatial optimization of resource allocation. We next present a new solution to illustrate how to combine the optimization of the temporal green energy allocation and spatial mobile traffic distribution. The whole optimization problem is decomposed into four subproblems, and correspondingly our proposed solution is divided into four parts: energy consumption estimation, green energy allocation, user association, and green energy reallocation. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.

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