Modelling and Simulation of Electrical Energy Systems through a Complex Systems Approach using Agent-Based Models

Complexity science aims to better understand the processes of both natural and man-made systems which are composed of many interacting entities at different scales. A disaggregated approach is proposed for simulating electricity systems, by using agent-based models coupled to continuous ones. The approach can help in acquiring a better understanding of the operation of the system itself, e.g. on emergent phenomena or scale effects; as well as in the improvement and design of future smart grids

Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

Reinforcement learning in local energy markets

Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility

Modeling smart grids as complex systems through the implementation of intelligent hubs

ICINCO 2010The 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

Towards complex system design and management in the engineering domain – the smart grid challenge

This is an Author's Accepted Manuscript of an article published in “Emergence: Complexity and Organization”, 15 (2), pp. 14-22 (2013), copyright Taylor & Francis.Our world is facing a significant challenge from climate change and global warming, coupled with an increased awareness about the importance of preserving the environment. This challenge calls for us to use our resources more efficiently and develop in a more sustainable way. One important part of this move towards sustainability is a radical change in the energy sector, characterized by the introduction of new technologies providing low carbon electricity generation and the use of dynamic distribution systems such as the smart grid. This shift requires the use of new tools, especially in the modelling and simulation areas. Complexity science can help us deal with the many new challenges arising, which are mainly related to a more distributed system with a large number of dynamic, interconnected resources. New approaches to deal with these issues are presented based on two case studies

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning TechnologiesKnowledge of Differential Equations is applied to various scientific fields such as physics, chemistry, biology and engineering and therefore often an important part in the basic subjects of mathematics in the first college courses related to those areas. The logic and common sense seems to indicate teachers use these basic skills acquired by students and employ them to curricula development in the following intensification courses, but unfortunately it is not usually the case. According to the authors, that is because instead of using a generic software to set up and solve the problems of Differential Equations that arise at different areas, what we have is a proliferation of software applied to solve special case problems. Some of these programs offer sophisticated graphical user interfaces to create complex system models, usually by putting together some library components, as if it were a puzzle, but without the need to set up the differential equations. According to the authors, this method, although valuable to solve some specific problems very quickly, is aberrant from the educational point of view, because it allows students to solve problems without knowing what they are doing or how they are doing. Worse, if a complication arises in the problem statement, for which there are no pieces in the puzzle, or execution errors occurs due to an incorrect construction, then they are not able solve the problem. Because of this, software that does not hide the equations and with the user can know at any moment what he/she is doing, from the mathematical point of view, is missing. According to the authors, any simulation program including the System Dynamics paradigm meets this condition because its GUI is very close to differential equations and the Initial Value Problem. The modelling of a system using this paradigm is simply to raise because an initial value problem associated with the system is quickly represented by the graphical user interface of the simulation program. This article presents some learning experiences focused on "problem based learning" using AnyLogic, which provides the System Dynamics paradigm to perform simulations of physical systems. The program provides a graphical environment that allows to perform animations very easily. The first on is to simulate the filling of a tank of water where the model is a first order non-linear differential equation. This case is instructive as it is very easy to raise the initial value problem and may be valid to review some concepts already forgotten by the students such as for example the derivative, integral, differential equation and initial value problem. Other simulation exercises posed to students are the control of a cart by a force, a parabolic shooting, and other mechanical, electrical and thermal examples

Smart decentralised energy management

The German–Finnish research project FUture Smart Energy shows, how flexible devices, consuming or producing electricity in electric grids, can be self-organised in a fully decentralised way, using autonomous algorithms integrated with the devices\u27 controllers. By shifting operation time, existing flexible devices are hereby utilised as ‘virtual batteries’, providing high storage capacity and power. To gain sufficient flexibility, a large number of devices like combined heat and power generators, heat pumps (HP), heaters, coolers, charging stations, pumps, household appliances and industrial plants, has to be coordinated. This results in a high system complexity for which the evaluated method provides an easy, resilient, cyber-secure and cost-effective solution. This novel technology uses a new market approach for electric energy systems. A real-time price signal is generated directly out of grid state variables, like frequency, voltage, power or current, and broadcast to the flexible devices. Without a need for central control, the flexible devices react like a natural swarm to the price signal. The system is easily and highly scalable, as adding and removing flexibilities does not imply adapting a central control system. The system can be operated parallel or in addition to existing energy markets

Agent-based modeling of the energy network for hybrid cars

Studies in complex energy networks devoted to the modeling of electrical power grids, were extended in previous work, where a computational multi-layered ontology, implemented using agent-based methods, was adopted. This structure is compatible with recently introduced Multiplex Networks which using Multi-linear Algebra generalize some of classical results for single-layer networks, to multilayer networks in steady state. Static results do not assist overly in understanding dynamic networks in which the values of the variables in the nodes and edges can change suddenly, driven by events, and even where new nodes or edges may appear or disappear, also because of other events. To address this gap, a computational agent-based model is developed to extend the multi-layer and multiplex approaches. In order to demonstrate the benefits of a dynamical extension, a model of the energy network in a hybrid car is presented as a case study

Increasing the efficiency of local energy markets through residential demand response

Local energy markets (LEMs) aim at building up local balances of generation and demand close to real time. A bottom-up energy system made up of several LEMs could reduce energy transmission, renewable curtailment and redispatch measures in the long-term, if managed properly. However, relying on limited local resources, LEMs require flexibility to achieve a high level of self-sufficiency. We introduce demand response (DR) into LEMs as a means of flexibility in residential demand that can be used to increase local self-sufficiency, decrease residual demand power peaks, facilitate local energy balances and reduce the cost of energy supply. We present a simulation study on a 100 household LEM and show how local sufficiency can be increased up to 16% with local trading and DR. We study three German regulatory scenarios and derive that the electricity price and the annual residual peak demand can be reduced by up to 10ce/kWh and 40