Utility–service provision is a process in which products are transformed by appropriate devices into services satisfying human needs and wants. Utility products required for these transformations are usually delivered to households via separate infrastructures, i.e., real-world networks such as, e.g., electricity grids and water distribution systems. However, provision of utility products in appropriate quantities does not itself guarantee that the required services will be delivered because the needs satisfaction task requires not only utility products but also fully functional devices. Utility infrastructures form complex networks and have been analysed as such using complex network theory. However, little research has been conducted to date on integration of utilities and associated services within one complex network. This paper attempts to fill this gap in knowledge by modelling utility–service provision within a household with a hypergraph in which products and services are represented with nodes whilst devices are hyperedges spanning between them. Since devices usually connect more than two nodes, a standard graph would not suffice to describe utility–service provision problem and therefore a hypergraph was chosen as a more appropriate representation of the system. This paper first aims to investigate the properties of hypergraphs, such as cardinality of nodes, betweenness, degree distribution, etc. Additionally, it shows how these properties can be used while solving and optimizing utility–service provision problem, i.e., constructing a so-called transformation graph. The transformation graph is a standard graph in which nodes represent the devices, storages for products, and services, while edges represent the product or service carriers. Construction of different transformation graphs to a defined utility–service provision problem is presented in the paper to show how the methodology is applied to generate possible solutions to provision of services to households under given local conditions, requirements and constraints.
Electrical power networks can be improved both technically and economically through the inclusion of distributed generator (DG) units, which may include renewable energy resources. This work uses multi-objective optimization by evolutionary computing, with power flow calculations and multi-dimensional results analysis, to investigate a method of defining optimal deployment of DG units. The results indicate that the method used is a feasible one for designing deployment of DG units in terms of type, number, and location.
Critical regimes are present in all socio-technical systems. Usually, man-made systems are designed to avoid these regimes completely, and stay in a stable steady state to avoid uncertainty. However, complexity theory postulates that the edge of chaos, between order and disorder, provides highly interesting phenomena, such as emergence, which are important for the evolution of the system. In this paper we explore the edge of chaos through a concrete example in electrical energy systems. The exploration is done through simulation, which provides a valuable mean to perform massive experiments on large scale systems. The complexity residing at the edge is discussed, and external, system relevant and internal factors which are likely to shift this edge or drive the systems trajectory towards or away from it are introduced.
The paper looks at the potential for multi-utility service providers to create business models that compete with traditional utility product providers whereby customers’ services increase and resource efficiency is improved. The objective in our modeling is to show the how Multi-Utility Service Companies (MUSCos) could invade the market, changing it from markets focused on selling, for example, energy to customers, to markets aimed at selling efficiency. The output of our modeling is the extent to which the resource efficiency of UK homes can be significantly improved, and we show this in 10 centile categories. The key difference between the two types of providers (Traditional and MUSCo) is that while households can switch traditional suppliers fairly rapidly (3 months), if a household signs up to get house improvements and utilities from a particular MUSCo, then the arrangement will be at least 5 years, and possibly longer. This means that households, once they sign up are no longer ‘on the market’ for utility supply. Although getting a contract signed may take much more time for a salesman than simply getting a normal utility order, the fruits and the income are guaranteed to the MUSCo for 5 or 10 years. Their sales force efforts can be switched to non-contracted households. So, essentially both utilities and MUSCos are trying to sell to the normal, non-contracted households. The real advantage that a MUSCo customer is that there is a discount on both the house improvements offered and the price of future supplies of energy and water. MUSCos are able to offer reduced cost of house insulation, appliances which reduce consumption and waste from multiple utilities, and can achieve marginal profits from financing of these appliances.
Attempts to model the present and future power networks face a huge challenge because it is a complex system, integrated by generation, distribution, storage and consumption subsystems, and using various control and automation computing systems. Moreover,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 electrical one, must be considered. In order to simulate those networks, a fully integrated agent-based model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is presented in this article. Moreover a way to try to extend it to cover infrastructure networks is outlined.
As half of the world’s population live in cities today, the topic of urbanization and urban energy systems shift continuously into society’s focus. It has become a common challenge for local governments to provide a so called “Master Plan”, outlining a long term vision for the city’s energy infrastructure, to which all planners and investors have to adhere. Being a top-down approach, these Master plans are first of all politically motivated documents, which focus on achieving given targets, such as CO2-emission reductions or higher shares of electric mobility. Originating from these targets, a set of milestones and measures is derived, e.g., the implementation of certain green technologies or refurbishments of buildings. The goal of this paper is to elaborate a model, which allows analysis of a Master Plan from a bottom-up perspective and thereby quantitatively assesses the plan with regards to its feasibility, while identifying possible bottlenecks in its implementation. The results can then serve the city planners to adapt their planning in order to avoid unforeseen problems, when putting the plan’s measures into practice. The approach pursued in this research is a combination of system dynamics and an agent-based simulation model of the city’s energy system, providing both a high spatial and temporal granularity. The model is developed with the multi-method modelling tool Anylogic and with Geographic Information System (GIS). The city itself is represented with its existing building and power infrastructure, which is then subject to the planned measures and developments. The core of the model implements on the one hand different energy generation technologies, both fossil fuels and renewables, reaching from big power plants to small local PV-installations on a private household’s roof. On the other hand, the heat and electricity consumers are represented through the buildings. The aim of the model is, at first, to provide a support system to analyze the short and long term effects of the Master Plan. Since its measures are usually not planned in detail concerning exact location or timing of the realization, the simulation results can provide references on these specific details. Secondly, the findings are used to identify the impact of single planned measures and their combinations which answers the questions of how, when and where local electricity and heat producers and the energy efficiency measures influence one another and if they have synergetic or competitive effects. Finally, a set of recommendations is derived from the analyses, which can help the city planners to transfer the strategic measures of the Master Plan into operative business.
The aims of this paper are to present and to discuss an agent-based model of population dynamics for the European regions at NUTS 3 level. It includes individuals that perform several activities with bounded rationality. The paper briefly discusses the latest novelties on this topic and then describes the processes to prepare a data base with the necessary information to feed and calibrate the model. Then it is presented the initialization module. It generates individual heterogeneity according to average and marginal aggregate distributions of the included variables that characterize the agents. In order to simulate the mechanisms of migration our model creates an artificial labor demand at regional level using simple but effective rules based on mainstream economic theory. The rest of the model is also presented: education, pairing, aging and deceases. A set of scenarios is defined and the regional aggregates are computed. Hence, the results are prepared to be visualized with tables, graphics and maps.
In this and the next issue of E:CO, we are reprinting two classic papers emanating from and even generating much of the ground swell of burgeoning enthusiasm for chaos theory forty years ago. Yes, it’s been that long! These papers were among the first to explore the nature of bifurcation, attractors, and chaos as discovered in complex systems through developing new mathematical tools and conceptual frameworks. These papers are truly foundational in their ramifications and implications and yet come across as fresh as if they were written yesterday.
When the world literally crumbles underfoot, and everything collapses into the dust, trapping and killing thousands, is your first instinct to say, “Thank goodness, that didn’t happen here!” Or let me travel halfway around the world to see how I might be able to help. When the email arrived that Lina was about to get […]