Decentralise Energy System

1 Electricity Challenges in decentralised

Electricity Systems

Electricity system production from wind and solar energy is intermittent, thus changing the paradigm of an electricity system where, traditionally, demand had been considered as the main fluctuating factor and raising, for instance, the system’s ramping requirements. Likewise, the installation of RES-E power plants drastically increases the degree of decentralisation of electricity generation. While in the past, large conventional power plants dominated the electricity system, distributed generation means that electricity feed-in on lower voltage levels is rising. As a consequence, congestion in distribution grids and reversions of the power flow direction1

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become more frequent stressing the existing system. There are also some trends of an increasing spatial divergence between production and consumption in Germany. Moreover, together with the stronger involvement of the demand side in electricity markets, the system is getting more heterogeneous in terms of generators’ and consumers’ objectives, responsibilities and experiences. These developments are accompanied by a growing penetration and an improvement of automated smart systems.

2 Approaches for modelling

Electricity Systems

In order to analyse the research issues put forward in Section 1 an appropriate modelling approach is required. Electricity systems comprise all technologies and institutional arrangements which are necessary and implemented in reality in order to supply consumers with electrical energy. In general, such systems are considered to be very complex, due to the different techno economic parameters, the diversity of stakeholders, the high societal value of electricity supply and interactions within the electricity system itself as well as with other systems (e.g. fuel or heat markets). Given this project’s focus on potential interactions within liberalised electricity spot markets and decentralised systems, the research design needs to consider relevant market operations, the behaviour of certain players and physical constraints of the distribution grid.

2.1 Energy systems analysis

Energy systems analysis is a comprehensive discipline which considers restrictions, dependencies and developments of complex energy systems in a structured, methodological way. Objectives of energy systems analysis can include describing a system, explaining the effects of different frameworks, identifying optimal pathways and, ultimately, providing structured support for decision making in the energy system. As such, energy systems analysis is an essential tool in the field of energy economics. At the centre of energy systems analysis, there are the development, implementation and application of a mathematical-computational model representing the relevant features of the energy system under consideration

3 A flexible modelling toolbox for decentralised electricity systems In order to address the project’s research issues the Power ACE model is extended in several ways. The key development is the integration of decentralised electricity systems into Power ACE in order to analyse the load flow in distribution grids and potential interactions with centralised markets. The model extensions have been designed in a way which allows using them within the market simulation or on a stand-alone basis. Furthermore, additional models for specific applications are developed outside the agent-based framework which could be integrated into Power ACE in future projects.

Together, the existing Power ACE model and the extensions presented in this report form a flexible toolbox for analysing distribution systems of different scales. In this section, we present key aspects of the overall simulation framework as well as of the individual extensions.

3.1 Overview of modeled decentralized

Electricity Systems

The modelled decentralised systems are always considered as a subsystem of the high-level market area simulated in the centralised Power ACE model. Consequently, there is an overlap from a geographical and also from an institutional point of view, e.g. local electricity demand needs to be satisfied by buying electrical energy on centralised markets. 

3.2 Electricity generation from wind and PV

When modelling electricity systems subject to transmission and distribution restrictions, it is relevant to determine where RES-E is generated within the grid infrastructure. This section deals with the challenges to generate RES-E feed-in profiles with a high temporal and spatial resolution. General input data for the implemented approaches includes power plant information on the one hand and weather data on the other hand (see also Section 4):

 Technology-specific plant data Wind: hub height, installed turbine, installation date – Photovoltaic (PV): net capacity, technology, module inclination, azimuth, installation date

 Weather time series data regarding wind speeds (m/s), solar irradiation (W/m²) and temperature (K), each on a 20 km x 20 km raster with a 10- minute resolution

The modeling of electricity generation basically consists of two steps:

 Preprocessing of the weather data

 Application to the generation model

3.2.1 Preprocessing of weather data

Typically, available weather data has to be transformed before using it as input for a generation model. The required input data depends on the specific

generation technology (wind or PV) currently simulated. 

Wind speed

Air density

Solar irradiation

3.3 Demand-side modeling

In current electricity systems, demand flexibility is considered to be low, i.e. most consumers have little incentives and possibilities, respectively, to change their common consumption behavior. However, there is long-ongoing research that a higher flexibility of demand could help to reduce the need for peak-load generation capacities, to limit investments in transmission and distribution networks as well as to support the overall integration of RES-E in electricity systems (e.g. Strbac 2008). One way to increase demand flexibility is through demand response programs, of which dynamic end-user electricity prices are one option. These prices would be aligned to wholesale electricity prices and, therefore, implicitly also to the RES-E feed-in. The price elasticity of electricity demand is one way to express the sensitivity of electricity consumers to prices (e.g. Lijesen 2007). In this context, methods to analyse the potential and the effects of demand flexibility need to be developed (e.g. for business development, in the context of energy systems analysis).

3.3.1 Bottom-up load model for residential electricity demand Standard load profiles, frequently used by utilities and modellers today, do not explicitly consider potential impacts caused by different tariffs and by the heterogeneity of consumers. Therefore, a new bottom-up load model for residential electricity demand is developed. The approach intends to generate weekly load profiles with a 15-minute resolution for a portfolio of heterogeneous households. Repeated model applications allow the creation of profiles for longer horizons, e.g. for one year (Hayn et al. 2014a).

The comprehensive model framework offers several features. Firstly, load profiles for different household types varying by size and by their equipment with electrical appliances can be generated. In total, the model integrates around 15 different types of appliances and offers additionally the opportunity to include residential PV systems for self-consumption. Secondly, appliances are differentiated according to whether and how they are available for load shifting by the user. In general, only appliances with a thermal storage (e.g. fridges, heating and hot water systems) or with flexible starting times (e.g. washing machines, dish washers) provide demand flexibility. Furthermore, a distinction is made between automatic price reaction by “smart” appliances and manual shifting. Each appliance is characterised by a simplified load profile, a household-specific peak load and an average utilisation

rate. Thirdly, the model reflects the reaction of households to external signals in the form of either variable energy prices or variable capacity prices indicating shortage situations in the electricity system. Both types of signals are determined externally by the electricity supplier.

3.4 Congestion management in distribution networks

The simulation framework to be developed in the course of this project is intended to replicate a wholesale spot market for one market area together with selected decentralised electricity systems as part of the total system in more detail. Thereby, the model features some simple forms of inter dependencies between the centralised and decentralised system layers.

On the one hand, decentralised systems affect the total system. The demand and generation of all units in the decentralised systems are offered to the centralized energy market either via direct participation of market agents (e.g. generators with large conventional power plants) or indirectly via intermediaries (e.g. owners of RES-E units). Consequently, depending on the relative size of decentralised units, results for the whole market area are influenced to a greater or lesser extent by the behaviour of the decentralised systems’ agents.

5.4 Increasing demand flexibility and its potential effects on decentralised electricity systems Demand response programs are expected to increase demand flexibility and ultimately to support the adaptation of the electricity system to current challenges (see also Section 3.3). However, research has shown that such programs could also cause some detrimental effects. New demand peaks can occur which might not be met by supply in the energy market or render transmission and distribution infeasible (“avalanche effects”; e.g. Ramchurn et al. 2011; Boait et al. 2013). Therefore, an analysis is undertaken in Ringler et al. (2015) regarding the effects of an increasing demand flexibility on local electricity systems and the role of the current market design in Germany. For the analysis, necessary data is processed and different adjustments to the PowerACE model are made. On this basis, several simulation runs with the PowerACE model are applied to study potential impacts on the modeled electricity system as defined in Section 1.

5.5 An analytical approach to congestion

management in distribution networks

This work aims at finding a congestion management algorithm that is applicable to a market design with uniform pricing which most European countries have adopted. Congestion management is defined as the act of modifying the dispatch of generation and demand within the modeled distribution grid when technical limitations of any grid elements are expected. Congestion management in a market with uniform pricing can be simplified as a process with the following three steps

1) Market-based dispatch: All generation and demand units participate in the wholesale electricity market to buy or sell electricity. Demand and supply is matched regardless of their location and contribution to a potential congestion. Based on the market results, an exact or estimated schedule is to be determined for each generation and demand unit in the system.

2) Grid constraints: Grid operators calculate the expected load flow within their grid on the basis of these schedules. If the dispatch is feasible without violating limits, the final dispatch has been found. If parts of the grid are congested, the DSO has to execute congestion management measures in order to manipulate the feed-in at the grid’s nodes. We differentiate between the following two approaches:

a. Nodal congestion management: The grid operator triggers flexible load elements on a nodal level and, thus, reaches a spatial resolution of the congestion management equal to the preceding load flow analysis (see Section 3.4.2).

b. Zonal congestion management: The grid operator triggers flexible load elements limited to a zonal level. A zone containing a number of nodes can receive signals only as a whole, meaning that all nodes receive the same signal. The spatial resolution of the congestion management is thus lower than the underlying load flow analysis.

3) The final dispatch is determined after the execution of a potential congestion management measure. If no congestion occurred, the final dispatch is solely based on the results of the wholesale energy market.

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