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The state of the art in science is to take into account forecast uncertainties in optimization either in the form of variant calculations or in stochastic optimization models. This paper presents an alternative approach for the consideration of forecast uncertainties within model predictive control. The approach defines a guaranteed flexibility range, which is responsible for the compensation of expected forecast errors. It is proven that the risk can be reduced with the help of quantiles based flexibility bands. To implement the approach, a controllable resource is required in the energy system. In this case it is an energy storage device. The result shows that with the help of the new approach it is possible to minimize the forecast uncertainties in the local energy system and thus reduce the impact on the overall system. With this algorithmic approach, the planning reliability in the overall energy system is to be increased.

Due to increasing contribution of renewable energy sources to the overall energy production, future control of energy consumption should achieve the principle 'generation following demand'. Within this approach, also known as demand side management, consumers are prompted to alter demands through an incentive signal. This work addresses the use of variable price as such incentive signal to influence power consumption of private households in a controllable manner. The mathematical modelling is based on simulations of optimal power flow and includes several optimisation constraints and criteria, according to which both direct and inverse models are constructed. Therefore, the problem of power signals and price signals is considered as fully-coupled. The analysis is performed for a model, representing typical conditions and scenarios of a residential network with household customers, including solar and wind power plants. The optimisation criteria are constructed with the emphasis on distribution system operators. The results of computations, performed with simplified and extended optimisation models, show a pronounced effect on the overall power consumption, reduction of the extreme values and minimisation of the peaks. Finally, further steps are proposed to extend the model to target various optimisation constraints.

Within the venture “REGEES” (REGenerative rEnewable Electricity System) a new approach for a Coordinated Market Grid Operation Management (CMGOM) was developed. This approach is investigated in a simulation considering uncertainties. Therefore, the energy time series generator as a method to generate time series for demand and feed-in is described and used to generate simulation input in the form of forecast-scenarios for load and feed-in. Uncertainties in the form of forecast errors and stochastic time series properties are taken into account. The optimization problem, which represents the mathematical description for the acquisition and balancing process of the Balance Responsible Party (BRP), is introduced. Finally, the energy system simulation is described for a test case in order to evaluate the CMGOM approach with consideration of uncertainty. For this purpose, deterministic optimization and two versions of optimization with uncertainties are compared.

An approach for an optimization problem to combine acquisition of a balancing group and grid restrictions given by distribution system operators is introduced in this paper. It is embedded into an extended scheduling system which enables communication between balance responsible parties and system operators. As the optimization problem should be used inside a DayAhead or IntraDay process it was formulated with linear restrictions and with a linear objective function to keep a short computation time. The optimization problem is very modular, hence it can be used for different use cases and is of course easily expandable. We give a proof of concept for the optimization problem and discuss advantages and disadvantages.

This paper presents the study results of possible plug in electric vehicle (PEV) integration potentials in a commonly occurring rural distribution grid in Germany as a worst case scenario simulation. This worst case consideration means, that all the simulations are running at a fix defined load point at one specific moment in time. After the simulation case definition, like e.g. the so called “strong load case”, the different in Germany commonly occurring PEV-classes are connected step by step to the 0,4 kV grid. So it is possible to compute the maximum integrable amount of PEVs (of each class) in dependency on the grid operation risks. As the core method the conventional Monte Carlo approach were used in a self-implemented characteristic.

The increasing change of the electrical power system caused by the influence of renewable energy on the power flow in the distribution grid effects mainly the distribution system operators. They have to deal with changed power flow as well as to account for congestions. Therefore they have to take influence on these power flows. One opportunity is represented by variable grid fees which enable a causation-based allocation of grid costs and a higher predictability concerning power system planning. According to the development of pricing in the communication sector flat-rate pricing for grid usage seems to be an appropriate solution. Therefore an approach based on a flat-rate tariff with an additional usage-sensitive component is proposed.

The increasing change of the electrical power system caused by the influence of renewable energy on the power flow in the distribution grid mainly affects the distribution system operators. They have to cope with the changed power flow as well as to account for congestions. Therefore they have to take influence on these power flows. One opportunity is represented by variable grid fees which enable a causation-based allocation of grid costs and a higher predictability concerning power system planning. According to the development of pricing in the communication sector flat-rate pricing for grid usage seems to be an appropriate solution. Therefore an approach based on a flatrate tariff with an additional usage-sensitive component is developed.

In this paper an approach for optimal management of electrical energy storages in distribution grid with high share of renewable energies is presented. Within this approach a multi stage combination of a Storage Scheduling as a long term optimization and a Storage Dispatch as the short term consideration is applied. For this a reference distribution grid with realistic low voltage level structures is applied. The modeling of this reference grid is presented for several levels of the management approach. By means of currently assumed development data for energy generation from renewable fluctuating sources scenarios for the reference distribution grid are developed and the presented approach is investigated in numerical case studies. In conclusion, results of the model calculations are analyzed.