研究目的
To minimize the time average expected energy cost of cloud data centers in smart microgrids (SMGs) considering the uncertainties in electricity prices, renewable energies, and arrival workloads.
研究成果
The paper presents a real-time algorithm for minimizing the long-term energy cost of cloud data centers operating in smart microgrid environments, considering practical factors like ramping constraints of backup generators, charging and discharging efficiency of batteries, and two kinds of data center workloads. The algorithm's feasibility and performance are validated through extensive simulations, showing advantages over other baselines.
研究不足
The study assumes that the cost functions are continuously differentiable and convex, which may not capture all practical scenarios. The algorithm's performance is analyzed under the assumption that uncertain parameters are i.i.d. over slots, which might not hold in all real-world conditions.
1:Experimental Design and Method Selection:
The study employs a stochastic programming approach to model the energy management problem, integrating constraints related to workload allocation, electricity buying/selling, battery management, backup generators, and power balancing. An online algorithm is designed under the framework of Lyapunov optimization technique to solve the problem without requiring future parameters' statistical information.
2:Sample Selection and Data Sources:
Real-world workload traces and dynamic electricity price traces are used in simulations to model the workload and price variations.
3:List of Experimental Equipment and Materials:
The study considers cloud data centers, smart microgrids, energy storage systems (ESS), conventional and renewable generators, and batteries with specific parameters like charging and discharging efficiency.
4:Experimental Procedures and Operational Workflow:
The algorithm involves observing system states, solving optimization problems to make control decisions, and updating system states based on the decisions made.
5:Data Analysis Methods:
The performance of the designed algorithm is analyzed through simulations, comparing it with two baselines under varying parameters like charging and discharging efficiency, ramping coefficient, and battery depreciation parameter.
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