Abstract
The ever-growing energy demand and the CO2 emissions caused by energy production and consumption have become critical concerns worldwide and drive new energy management and consumption schemes. In this regard, energy systems that promote green energy, customer-side participation enabled by the Internet of Things (IoT) technologies, and adaptive consumption mechanisms implemented on advanced communications technologies such as the Network Function Virtualization (NFV) emerge as sustainable and de-carbonized alternatives. On these modern schemes, diverse management algorithmic solutions can be deployed to promote the interaction between generation and consumption sides and optimize the use of available energy either from renewable or non-renewable sources. However, existing literature shows that management solutions considering features such as the dynamic nature of renewable energy generation, prioritization in energy provisioning if needed, and time-shifting capabilities to adapt the workloads to energy availability present a complexity NP-Hard. This condition imposes limits on applicability to a small number of energy demands or time-shifting values. Therefore, faster and less complex adaptive energy management approaches are needed. To meet these requirements, this paper proposes three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs) that, when deployed at the NFV domain, seeks the best possible scheduling of demands that lead to efficient energy utilization. The performance of the algorithmic strategies is validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and processing of demands. Additionally, simulation results reveal that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands.
Highlights
T RADITIONALLY, worldwide energy provisioning has been dominated by fossil fuels such as petroleum, coal, and natural gas, resulting in an increase in CO2 emissions and global warming [1]
Given that the proposed adaptive energy management model has an NP-Hard complexity [13], and the exact solution OptTsCost has exponential growth, this paper presents three heuristic strategies: (i) a greedy strategy based on a constructive algorithm and defined as GreedyTs, (ii) a genetic-algorithm-based solution identified as GATs, and (iii) a solution based on a dynamic programming approach and described as DPTs
For the adaptive energy management in Internet of Things (IoT) scenarios, this paper presents eight algorithmic strategies: first, an optimal solution defined as OptTsCost, three heuristic approaches described as GreedyTs, GATs, and DPTs, and, four heuristic solutions obtained as a result of the application of the prepartitioning method on the original strategies, which are identified as OptTsCostPart, GreedyTsPart, GATsPart, and DPTsPart
Summary
T RADITIONALLY, worldwide energy provisioning has been dominated by fossil fuels such as petroleum, coal, and natural gas, resulting in an increase in CO2 emissions and global warming [1]. Considering that an NFV-enabled and environmentally friendly adaptive energy consumption solution can increase overall energy utilization and prevent energy outages while producing cost reductions for the ES and ECs, developing less complex and faster management strategies is of the utmost importance This is the main contribution of this work. The simulation results demonstrate that applying the adaptive energy management model through the proposed algorithmic strategies (optimal and heuristics) produces an improved overall performance of the generation and consumption ecosystem, reflected in increased processed demands and a decrease in residual power. Three heuristic strategies identified as GreedyTs, GATs, and DPTs to solve the adaptive energy management problem in reduced running time compared to the optimal solution, OptTsCost, and with applicability to IoT scenarios with thousands or hundreds of thousands of energy demands.
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