Peter Bazan

Hybrid simulation of renewable energy generation and storage grids

The share of renewable energy sources in energy production is growing steadily. Domestic homes can be equipped with solar panels, micro combined heat and power systems, batteries, and they can become adaptive consumers. They can also deliver energy to the grid and react to the energy supply. This paper presents a hybrid simulation approach for the analysis of a grid of domestic homes equipped with different technological options with respect to efficiency and costs. For energy storage and energy flows the system dynamics modeling paradigm is used whereas control decisions are modeled as statecharts. The highly intermittent solar irradiation and also the electric power and heat demands are implemented as stochastic models. The component-based design allows for quick creation of new case studies. As examples, different homes with batteries, micro combined heat and power systems, or energy carrier carbazole as energy storage are analyzed.

SGsim: A simulation framework for smart grid applications

Simulation tools are an essential component of any emerging technology. In this paper we present SGsim, a framework to simulate different applications in the context of smart grid. The framework supports real time simulation. Therefore it is possible to evaluate time-critical applications such as real time monitoring and control. The framework combines two main simulators (1) OMNeT++, a discrete event simulator that is used mainly to simulate data communication systems and (2) OpenDSS, a tool to calculate the power flow in power grids. Moreover, the framework supports smart grid related standards such as IEEE C37.118. This way it is possible to integrate standard smart grid tools such as openPDC. Furthermore, an optimization toolbox is integrated in the simulator in addition to the capability to communicate with other tools such as MATLAB and R. We performed a set of case studies to show the capabilities of the simulator.

Exploiting day-ahead electricity price for maximized profit of photovoltaic systems

This paper proposes a methodology for optimal power management in a house with photovoltaic cells and battery storage. The proposed method exploits the day-ahead electricity market to enhance the profit of a household. An hourly-discretized optimization algorithm is proposed to identify the optimum daily operational strategy to be followed by the photovoltaic system, provided that a forecast for solar-power and load is available. This model is suitable to be applied in the real time operation of a typical house. The proposed strategy maximizes the individual revenue without shifting power demand. We explored the proposed algorithm with and without feed-in tariff. The results show that a typical house can save about 300 e/year when applying optimized power management.

SGsim: Co-simulation Framework for ICT-Enabled Power Distribution Grids

Empowering power grids with ICT is fundamental for the future power grid. Simulation plays an essential role for evaluating emerging smart grid applications. The presented co-simulation framework SGsim is based on two main simulators, OMNeT++ and OpenDSS. With newly added components, smart grid applications in the electricity distribution network can now be investigated and evaluated. Conservation Voltage Reduction (CVR) is a mechanism to reduce the power demand which eventually will reduce the energy consumption. In a case study, the co-simulation framework is used to explore the potential energy saving by applying a closed-loop CVR inside a residential power grid.

On the profit enhancement and state estimation services in the smart grid

New services and tools are emerging for advanced operation of next generation power grid. In this paper we introduce two of these services that can improve the operation of a power grid with distributed energy resources. Firstly, we explored the benefits of employing optimization approaches to enhance the profit of a power system that includes a Photovoltaic park, Wind Energy Plant, and Combined Heat and Power unit to serve a number of houses. Secondly, we explored the potential of utilizing the Phasor Measurement Units for state estimation issues.We presented the potential of applying these services in the next generation power grid and then we discuss the challenges and requirements with respect to communications in order to integrate these services in the power grid.

Approximate transient analysis of large stochastic models with WinPEPSY-QNS

In this paper we present a new algorithm for the approximate transient analysis of large stochastic models. The new algorithm is based on the self-correcting analysis principle for continuous-time Markov chains (CTMC). The approach uses different time dependent aggregations of the CTMC of a stochastic model. With the method of uniformization the transient state probabilities of each aggregated CTMC for a time step are calculated. The derived probabilities are used for the construction of stronger aggregations, which are applied for the correction of the transition rates of the previous aggregations. This is done step by step, until the final time is reached. High aggregations of the original continuous-time Markov chain lead to a time and space efficient computational effort. Therefore the approximate transient analysis method based on the self-correcting aggregation can be used for models with large state spaces. For queuing networks with phase-type distributions of the service times this newly developed algorithm is implemented in WinPEPSY-QNS, a tool for performance evaluation and prediction of stochastic models based on queuing networks. It consists of a graphical editor for the construction of queuing networks and an easy-to-use evaluation component, which offers suitable analysis methods. The newly implemented algorithm is used for the analysis of several examples, and the results are compared to the results of simulation runs where exact values cannot be achieved.

SWARM - Increasing Households' Internal PV Consumption and Offering Primary Control Power with Distributed Batteries

This Research in Progress Paper deals with a simulation approach for a virtual mass storage composed of small distributed battery energy storage units, installed in households with a roof-top photovoltaic system. On the one hand the households internal consumption of photovoltaic energy is maximized and on the other hand primary control reserve power is provided by a central storage controller. This concept is academically approved and rolled out in the field within this project. First simulation results show a households benefit of installing a battery energy storage system and an accurate working of the implemented virtual mass storage.

Profit enhancement through optimized operation of photovoltaic systems with elastic demand

Motivated by the decreasing costs of distributed energy resources and energy storage technologies, the increasing costs of electricity for households and the decreasing benefits of feed-in tariff, new strategies for power management inside a house are required. The two-way flow of information and power in smart grid creates new opportunities, services and applications in the next generation power grid. Both system operators and normal households can benefit from this paradigm, which has the potential to change not only how energy will be produced but also how energy will be consumed. In this paper we extend our previous approach aiming to enhance the profit of a privately owned Photovoltaic (PV) system such that it considers load elasticity and limitations on charging and discharging of batteries. The proposed method increases the profit through exploiting demand and supply forecasts, day-ahead price and demand elasticity. Each house finds periodically the optimal strategy to follow based on the results of solving a discrete optimization problem which takes into consideration different system parameters and constraints. The proposed power management controller is suitable to be applied in the realtime operation of a typical house. The results demonstrate that the proposed approach provides more savings compared to a standard approach across a range of different scenarios and system constraints. The results show that a typical house can save up to 300 €/year when applying optimized power management compared to a standard approach.

Multi-valued Decision Diagrams for the Verification of Consistency in Automotive Product Data

Highly customizable products and mass customization - as increasing trends of the last years - are mainly responsible for an immense growth of complexity within the digital representations of knowledge of car manufacturers. We developed a method to detect and analyze inconsistencies by employing a Multi-Valued Decision Diagram (MDD) which issued to encode the set of all valid product configurations. On this basis, we stated a number of rules of consistency that are checked by a set-based verification scheme.

Approximate analysis of stochastic models by self-correcting aggregation

We present an approximation algorithm for the analysis of large stochastic models. The fixed point iteration approach uses different approximate aggregations of the state space of a model. The stationary state probabilities of these aggregated models are calculated to derive refined aggregations which are used for the correction of the approximate aggregations. The presented method is then extended to benefit from components of higher level model descriptions by defining pairwise overlapping aggregations of the state space of a model. This construction of the aggregated models makes the automatic generation of appropriate aggregations possible, such that the interactions of the submodels are taken into consideration. Together with a well known aggregation formula and new and simple correction formulas the method is easy to implement. The good accuracy of the presented algorithm is shown by means of large examples and the results are compared with the results derived by simulation.