2020, Muff, Roswitha, Wache, Holger

More and more prosumers will penetrate the power grid. But how do prosumers affect the accuracy of the day-ahead load forecast? In contrast to related research on prosumers and load forecast, this paper addresses the impact of different shares of prosumers on the load forecast for areas with several households. In order to answer this research question, the load forecast accuracies for a dataset without prosumers is compared to the ones of datasets with different shares of prosumers in an experimental setup using neural networks. A sliding window approach with lagged values up to seven days is applied. Apart from electricity consumption data weather and date data are considered. The conducted tests show, that the mean absolute percentage error increases from about 8% for a dataset without prosumers up to about 39% for a dataset with a share of prosumers of 80%. It can therefore be concluded that prosumers decrease the accuracy of the day-ahead load forecast with neural networks.

2019, Layec, Vincent, Wache, Holger

A major issue hampering a rapid substitution of fossil fuels by electricity from sustainable sources is the fear of congestion of the power grid and of associated costs of their reinforcement. The conventional approach prevents any rapid raise of electricity demand by encouraging other energy carriers and sector coupling. However, no approach investigates the utilization of the full capacity of the power grid alone, which are kept idle to provide sufficient reserve for the case of a failure. Therefore, we test a load management approach designed to utilize this reserve capacity. We verify in this paper the correct functionality of the system made of a device manager for cost optimization of schedules and of a grid manager to enforce the respect of power limits of the grid. This novel approach contributes to reduce emission of greenhouse gases without grid reinforcement.

2018-02-01, Wache, Holger, Künzli, Michael, Schulz, Nicola, Bichsel, Jürg, Hall, Monika

The increasing number of volatile energy sources, such as solar power plants, challenges the power network operators, the energy brokers as well as the electricity market actors. In this work, a multi-agent based approach will be introduced that allows multiple households to trade flexibilities, on top of the usual selling of produced energy to the paying consumer. Flexibility trading allows different optimisations for different actors. They can slightly shift their electricity consumption, e.g. by turning boilers on/off, to optimise the system (e.g. follow a predefined schedule). Simulation results indicate that a flexibility market consisting of only few households can already help to optimise the system.

2015, Lammel, Benjamin, Schulz, Nicola, Bichsel, Jürg, Wache, Holger, Farooq, Abdul, Hoffmann, Caroline, Mettler, Fabian

In this article, the "SmartStability" concept is introduced and first results are shown. The concept is based on the exchange of electrical energy within a network of households that possess temporal flexibilities in consuming or providing energy from or to the network. The exchange is governed by a market-economic negotiation principle between the households. Temporal flexibility is achieved by exploiting thermal capacities of the buildings themselves and those of warm water storages, and by allowing certain temperature bands. Electric and thermal energy forms are coupled by means of heat pumps and electric water boilers. The physical energy exchange takes place via the electrical grid. The behaviour of a SmartStability network has been simulated, based on physical models of the energetic resources within each network unit, and by interlinking the individual units to form the entire SmartStability network within a multi-agent environment. Goal of several simulation scenarios was the adaptation of the time-dependent power consumption profile of the network to a given schedule. Networks consisting of 5 to 100 houses have been simulated. The simulation results show that deviations from schedule can be reduced by approx. 50% by the market-economics-based self-optimization and the resulting intelligent operation of resources. By additionally using battery storages, the deviation from schedule can be further significantly reduced.

2019-11-21, Hall, Monika, Geissler, Achim, Wache, Holger

With the increasing number of photovoltaic systems and heat pumps in buildings existing substations of the electric grid could be overloaded. A multi-agent based simulation of a building cluster studies the impact of building flexibility in regard to the residual substation load. Each building announces its available flexibility, e.g. "heat pump can be switched off/on". A master coordinator evaluates all incoming offers and decides which offers are accepted. This reduces the residual load at the substation. This paper presents results from a study of the impact at the substation of a smart urban building cluster with different penetration scenarios of heat pumps, photovoltaic systems, batteries and electric vehicles. It is shown that a high penetration of heat pumps and photovoltaic systems violates the substation's limits for the studied building cluster. Batteries cannot reduce the peak utilization. The master coordinator's load shifting options are limited.

2018-02-01, Bagemihl, Joachim, Boesner, Frank, Riesinger, Jens, Künzli, Michael, Wilke, Gwendolin, Binder, Gabriela, Wache, Holger, Laager, Daniel, Breit, Jürgen, Wurzinger, Michael, Zapata, Juliana, Ulli-Beer, Silvia, Layec, Vincent, Stadler, Thomas, Stabauer, Franz

The continuous increase of competitiveness of renewable energy in combination with the necessity of fossil fuel substitution leads to further electrification of the global energy system and therefore a need for large-scale power grid capacity increase. While physical grid expansion is not feasible for many countries, grid-driven energy management in the Smart Grid often interferes in customer processes and free access to the energy market. The paper solves this dilemma by proposing a market-based load schedule management approach that increases power grid capacity without physical grid expansion. This is achieved by allocating for a certain class of non-critical flexible loads called “conditional loads” the currently unused grid capacity dedicated to ensuring N−1 security of supply whereas this security level remains untouched for all critical processes. The paper discusses the necessary processes and technical and operational requirements to operate such a system.

2018, Schädler, Philippe, Wache, Holger, Merelli, Emanuela, Mielczarski, Władysław, Wierzbowski, Michał, Olek, Błażej

In order to derive indicators for the future grid and market stability, in this paper an agent-based model is intro- duced, to simulate various scenarios. This includes new market designs, market mixes, emerging technological inventions and new regulations. Consumers demand energy based on seasonal variations or changing prices. The suppliers’ production might also depend on seasonal variations, on the local solar irradiation or its flexibility; the ability to react to the market requests. The model introduced in this paper has been used to describe an example scenario of the year 2035, representing a market mix that includes a variety different consumers and suppliers. Eventually it shows, how the model can be applied to model various scenarios and how the resulting grids frequency, the market prices and suppliers profit can be used as indicators for the grid and market stability.

2019-05, Christen, Ramón, Layec, Vincent, Wilke, Gwendolin, Wache, Holger, Donnellan, Brian, Klein, Cornel, Helfert, Markus

The electrification of the global energy system and the shift towards distributed power production from sus- tainable sources triggers an increased network capacity demand at times of high production or consumption. Existing energy management solutions can help mitigate resulting high costs of large-scale physical grid rein- forcement, but often interfere in customer processes or restrict free access to the energy market. In a preceding paper, we proposed the RLS regional load shaping approach as a novel business model and load management solution in middle voltage grid to resolve this dilemma: market-based incentives for all stakeholders are pro- vided to allow for flexible loads that are non-critical in customer processes to be allocated to the unused grid capacity traditionally reserved for N-1 security of supply. We provide a validation of the technical aspects of the approach, with an evaluation of the day-ahead load forecasting method for industry customers and a load optimization heuristics. The latter is tested by a simulation run on a scenario of network branch with provoked capacity bottlenecks. The method handles all provoked critical network capacity situations as expected.

2018-02-01, Hunziker, Christoph, Schulz, Nicola, Wache, Holger

We present a new method to control an aggregated electric load profile by exploiting the flexibilities provided by residential homes. The method is based on a common energy price combined with inclining block rates, broadcasted to all households allowing them to minimize their energy provisioning cost. The distributed home energy management systems receive the price signal and use mixed integer linear programming for optimal scheduling of load, storage, and generation devices. The method provides excellent scalability as well as autonomy for home owners and avoids load synchronization effects. As proof of concept, an optimization algorithm for determining a day-ahead price is applied in two case studies. An excellent conformance between a given reference load profile and the resulting aggregated load profile of all households is demonstrated.

2015, Keller, Corinne, Manser, Daniel, Vogler, Sandro, Wache, Holger

Abstract—Several European countries are increasingly focusing on renewable energy in order to satisfy their demand. A core problem of these sources is their reliability, which means less continuously available energy is accessible. Smart grids are trying to cope with this problem by adding intelligence to the net, which tries to adjust the load according to the current produced amount of electrical energy. Many approaches try to tackle down this problem by technical means. This paper analyses existing economical approaches for smart grid environments and highlights the unique features and important properties of a broad selection of papers. Classification criteria are derived from existing literature. Afterwards, the most prominent papers are used to demonstrate how the classification scheme can be applied.