The storage of electrical energy is becoming increasingly important to satisfy the demand through renewable energy sources. In this paper, a continuous and discrete simulation of a pumped thermal energy storage (PTES) system are compared with respect to their computational time and accuracy.
Operations and maintenance (O&M) expenses can vary greatly from one energy solution to another. While a solar farm or geothermal system may need minimal ongoing maintenance, wind turbines require a skilled crew to keep them operating efficiently.
In this research, the authors use a scaled-down wind farm case study to demonstrate the potential of Reinforcement Learning (RL) in identifying an optimal O&M policy and to show the ease of use of AnyLogic simulation software and Pathmind reinforcement learning tool.
The purpose of this research is to investigate how electric vehicles can promote energy transition and how blockchain can facilitate the decentralisation of future energy systems.
With the slow but steady rollout of smart meters and advancements in internet of things (IoT) technology, and with the help of agent-based modeling, the results from this study will prove the worth of blockchain’s inclusion in the smart grids of the future.
Worldwide Electrical Power Systems (EPSs) are faced with tremendous challenges because of the reduction of greenhouse gas emissions and the increasing number of renewables. EPS analysis can help to show future developments in an uncertain environment and is an important task for the assessment of greenhouse gas emissions. In order to perform such a complex analysis of future EPSs, a huge number of input parameters is needed. Moreover, technical and also economical processes have to be considered. Thereby, one major task is the modeling of electricity markets. In this paper, we present an approach for the modeling of the German EPS including electricity markets using hybrid simulation and mathematical optimization. We contribute an object-oriented electricity market model which can be utilized to study different exchange mechanisms and behavior patterns of generation unit operators. Simulation results show market results for different generation unit operators and realistic market prices.
Many complex real-world problems which are difficult to understand can be solved by discrete or continuous simulation techniques, such as Discrete-Event-Simulation, Agent-Based-Simulation or System Dynamics. In recently published literature, various multilevel and large-scale hybrid simulation examples have been presented that combine different approaches in common environments.
All over the world, and in particular in Germany, a trend toward a more sustainable electric energy supply including energy efficiency and climate protection can be observed. Simulation models can support these energy transitions by providing beneficial insights for the development of different electricity generation mix strategies in future electric energy systems.
The solution to the problem of electricity supply shortage in remote regions of Lebanon is described in detail using a discrete-event simulation model of a constructinon process developed in AnyLogic. The work illustrates the different construction stages from rough grading, access roads construction, foundation and electrical works, to wind tower assembly and erection. The whole process is then optimized to mainly minimize the project duration.
Due to the transition towards a sustainable energy supply, many electricity generation systems are faced with great challenges worldwide. Highly volatile renewable energy sources play an important role in the future electricity generation mix and should help compensate the phase-out of nuclear power in countries such as Germany. Simulation-based energy system analysis can support the conversion into a sustainable future energy system and are intended to find risks and miscalculations. In this paper we present main components of the electricity generation system models. We use a hybrid simulation approach with system dynamics and discrete event modules. This modular design allows quick model adoptions for different scenarios. Simulation results show the development of the future annual electricity balance, CO2 emission balance, electricty imports and exports, and the wholesale price of electricity.
The electrical system is undergoing a profound change of state, which will lead to what is being called the smart grid. The necessity of a complex system approach to cope with ongoing changes is presented: combining a systemic approach based on complexity science with the classical views of electrical grids is important for an understanding the behavior of the future grid. Key issues like different layers and inter-layer devices, as well as subsystems are discussed and proposed as a base to create an agent-based system model to run simulations.