Agent-based simulation and simulation optimization approaches to energy planning under different scenarios: A hospital application case

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Highlights

  • Agent-based simulation is used to control multi-energy systems.

  • Electricity, heating, natural gas, and converters are considered.

  • Cogeneration and photovoltaic are notable for decreasing lower carbon emissions.

  • Building energy planning problems are solved with simulation optimization.

Abstract

In complex multi-energy systems, one of the main goals is that combining optimal circumstances to acquire minimum system cost as decreasing energy consumption without an outage of energy supply. This study mainly has focused on flexible solutions that include the controlling of a multi-energy system and its components such as electricity, heating, natural gas, and converters to minimize the overall system cost and reduce carbon emissions by observing of demand whether is satisfied. The technique of agent-based simulation and simulation optimization approaches has been adopted as solution methods for tackling these difficulties. As a case study to implement the flexible solutions, the multi-energy system of a large hospital complex placed in Turkey was selected. These flexible solutions based on the integration of cogeneration and photovoltaic systems to existing energy infrastructure have been simulated under multi-tariff and singe-tariff electricity price strategies for different years. The used data in the model has been gathered from the hospital electricity and natural gas bills, the energy projects realized for the hospital in the past, and other various sources including PVGIS database, Turkish State Meteorological Service, commercial cogeneration/PV panel manufacturers, and publicly available reports. The results indicate that the effective presence of cogeneration and photovoltaic is notable in the decreasing total system cost taking into consideration also lower carbon emissions. Once current system was simulated, it was compared with other proposed possible configurations with different two scenarios. Based on the simulation models that were optimized with different metaheuristics such as genetic algoritm, the economic results show that the both of two scenarios can be profitable (simple pay back periods around 5.28 and 6.6 years respectively) even without any public funding. Furthermore, it was determined that cogeneration-based scenario-2 provided a 12.35 % reduction in carbon emissions compared to the base scenario, while scenario-3 provided a 16.94 % reduction compared to the base scenario, and both scenarios are proven to be quite environmental friendly.

Section snippets

Introduction and literature review

The factors such as limited resources, excessive and growing consumption, high costs make it mandatory to have an energy management perspective and use energy efficiently under flexible and applicable projections. Large buildings like hospitals, hotels, and trading centers are big energy consumers, which have a responsibility to take control of their energy consumptions and investigate alternative energy sources that are reducing costs and consumption. In EU countries, all buildings are

Methodology

To achieve the mentioned objectives based on flexible energy planning solutions, agent-based modeling and simulation optimization approaches have been employed.

Energy consumptions

Electricity and natural gas monthly consumption data were gathered from the bills between the years of 2012–2018 in the referenced hospital. The gathered monthly data in kWh unit has been analyzed and classified based on monthly consumptions to group similar months that are in terms of both electricity and natural gas respectively. Fig. 2 shows yearly consumption based on months depending on average consumption of 7 years for electricity and natural gas. As shown in Fig. 2, electricity

Results and discussion

The results belong to simulation models that have been run for 2019 and 2020 data separately by using of two different electricity tariff strategies for all the scenarios are shown in this section. Furthermore, to obtain the minimum system cost, we employed a simulation–optimization approach by determining the decision variables that are cogeneration capacity and the number of panels. The results of the optimization are also shown in this section.

Conclusions and future study

In this study, we have tried to construct the flexible tools that enable to manage and planning for building energy systems with the help of agent-based simulation and simulation optimization approaches. The proposed approach leverages the power of agent based simulation modeling and metaheuristics that allow to optimize the simulation models with aim of minimizing total system cost depending on cogeneration capacity and number of photovoltaic panels.

Firstly, due to lack of data, based on a

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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    Current address: Gaziantep University, Faculty of Engineering, Department of Industrial Engineering, Gaziantep 27310, Şehitkamil, Turkey.

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