Network Planning in Smart Energy Systems
Hosted by Theo Fens (TU Delft / TFECo B.V.)
From an infrastructural perspective the energy transition concerns the advent of transition aspects such as decentral generation by PV and micro-CHP, chargers for electric automotive, the application of heat pumps at various scales and local storage of electricity and heat. The alleged intelligence of smart energy systems is supposed to deal with the underlying information exchange required for the infrastructure to function properly.
The above mentioned transition aspects impact the infrastructure governance: can the network accommodate the load changes and can these local decentrals be integrated? And what will be the implementation paths of transition aspects such as the purchase of PV, E-cars and storage as a function of time? Finally, which institutional arrangements may have to be adapted? In order to address these issues we developed a model that enables integrated simulation of the physics concerning the load on the network, combined with socio-economic behaviour concerning the purchase of transition objects such as PV or an electric car.
In the load modelling the network is configured from geographical information systems reflecting the actual geographic topology of the network. End user energy consumption is based on representative data obtained from smart metering. This approach for configuration leads to an actual representation of reality. In addition, the PV systems are combined with battery storage at various locations in the network: at the end user premises, the neighbourhood battery, the mid voltage station battery and the high voltage station battery.
Agent based modelling is employed to reflect socio-economic behaviour of consumer to purchase PV systems and electric vehicles. The resulting adoption curves are integrated into the load network modelling. As such scenarios with different adoption curves can be evaluated concerning network load, investment for network enforcement and economic impact on actors like the consumer, the energy supplier (turnover) and the government (tax income).
The business model of the energy supplier is substantially affected by the energy transition. The energy transition brought about local energy communities. It also gave rise to a new business concept: energy service companies, ESCo’s. Could local energy communities evolve into ESCo’s?
In this seminar we will discuss the latest developments of this model, present the results of recent work in which a set of scenarios were simulated and discuss the possible impact on the ESCo business model.
The following speakers will contribute to the workshop:
Agent based modelling, socio economic behaviour.
Tristan de Wildt (TU Delft)
The complexity and unpredictability of human behaviour introduces large uncertainties in the electricity supply system, both in electricity consumption and the adoption of (smart) energy appliances. As a result, it is difficult to plan grid investments efficiently.
Simulation models can be used to explore the dynamics of human behaviour that influence adoption. Agent-based modelling is one class of simulation models that has two main advantages over other simulation methods in the modelling of energy appliances adoption: it allows to easily integrate human heterogeneity (not all individuals react to the same incentives) and social networks (adoption might be influenced by the social network of individuals).
In an agent-based model, numerous agents (representing households for example) co-evolve in a single environment. An agent is characterized by a state and an internal logic. The internal logic determines the conditions under which the state of the same agent changes at each iteration, for example as a result of interactions with neighbouring agents. This internal logic can be written in such a way that the agent makes non-rational choices, which characterizes socio-economic behaviour.
Combined network and agent based modelling, an example case.
Michiel van Dam (TU Delft / Alliander)
Distribution System Operator Alliander carried out a research project where they use an in house developed simulation model called the SmartCap Toolbox. One of the objectives of this project is to test the technical and economic impact of battery storage at various locations in the network. The SmartCap Toolbox can run simulations in which consumers purchase solar PV and battery storage and assess the effects on the network as well as the financial consequences for the actors. The amount of consumers that is expected to adopt solar PV and battery storage in the SmartCap model is based on adoption rates following an S-curve towards an equilibrium of a predetermined and fixed percentage over a time period of fifteen years. Goal of this MSc thesis project is to gain insights in the future opportunities of installing batteries in energy distribution networks. To achieve this goal, it would be favorable if battery adoption curves and PV adoption curves are based on socio-economic aspects that drive consumers in adopting new technologies instead of using predefined S-curves. In order to do so, agent based modelling is used to simulate behavior of individual consumers. The consumers are influenced by each other and by their environment on both social and economic grounds. The agent based model is used to enhance the insights in the effects of external inputs like battery prices, electricity and PV prices, environmental consciousness and the effects of policy measures like netting arrangements on the adoption of PV and battery storage.
Institutional consequences: could local energy communities evolve into ESCo’s?
JanPaul Buijs (Enexis / Erasmus University Rotterdam)
The ESCO (Energy Service Company) is a promising new role and business model in the energy transition era. The basic proposition consists of an energy saving potential scan followed by proposed measures to realize energy and cost savings over a period of time. Although many new ESCOs went bankrupt during the economic crisis of recent years, a consolidation was seen among bigger players in the installation industry. In the presentation, different earning models are presented and the future of ESCOs is discussed.
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