Abstract
One fundamental dimension in the design of an electrical energy system (EES) is the economic analysis of the possible design alternatives, in order to ensure not just the maximization of the energy output but also the return on the investment and the possible profits. Since the energy output and the economic figures of merit are intertwined, for an accurate analysis it is necessary to analyze these two aspects of the problem concurrently, in order to define effective energy management policies. This paper achieves that objective by tracking and measuring the energy efficiency and the cost effectiveness in a single modular framework. The two aspects are modeled separately, through the definition of dedicated simulation layers governed by dedicated virtual buses that elaborate and manage the information and energy flows. Both layers are simulated concurrently within the same simulation infrastructure based on SystemC-AMS, so as to recreate at runtime the mutual influence of the two aspects, while allowing the use of different discrete time scales for the two layers. Thanks to the tight coupling provided by the single simulation engine, our method enables a quick estimation of various cost metrics (net costs, annualized costs, and profits) of any configuration of EES under design, via an informed exploration of the alternatives. To prove the effectiveness of this approach, we apply the proposed strategy to two EES case studies, we explored various management strategies and the presence of different types and numbers of power sources and energy storage devices in the EES. The analysis proved to allow the identification of the optimal profitable solutions, thereby improving the standard design and simulation flow of EES.
Highlights
In the design of large-scale electrical energy systems (EESs), cost is a dimension at least as important as the energy efficiency of the system: given the initial investment, users do want an effective solution that can provide a return on the investment in the shortest possible time.Designing an EES encompasses a number of options, such as the choice of components, their sizing, and their management, possibly in a way that is aware of the load profiles
All simulations reported have been implemented in SystemC-AMS 2.1 and run on a server installed with Intel Xeon 2.40 GHz CPU (16 cores, 2 threads each) and 128GB RAM, with Ubuntu operating system 18.04.1
For the sake of readability, we report in E the evolution of the battery state of charge (SOC): from this plot, it is evident that electricity is bought from the grid when the battery is discharged (SOC < 10%) and the loads demand too much power, and that electricity is vice versa sold to the grid when power sources can feed the loads and the battery is fully charged (SOC > 90%)
Summary
In the design of large-scale electrical energy systems (EESs), cost is a dimension at least as important as the energy efficiency of the system: given the initial investment, users do want an effective solution that can provide a return on the investment in the shortest possible time.Designing an EES encompasses a number of options, such as the choice of components (which power sources and which storage devices), their sizing, and their management (how the energy flow is controlled among all the actors), possibly in a way that is aware of the load profiles. It is quite evident that accounting for (i) such a set of heterogeneous variables, (ii) numerous significant non-idealities, and (iii) complex inter-dependencies between components can only be handled effectively by the simulation of the EES as a cyber-physical system (CPS). This would allow one to describe accurate (power and cost) models for the components, fed by accurate traces of environmental data for the power sources, and exercised under realistic power demand traces [1,2]; on top of that, management policies modeled in software can evaluate a number of alternative scenarios.
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