Marc Schmidt

Investigations on fine particle separation using an electrostatic nozzle scrubber

Abstract Numerical simulations of particle/drop interactions shows an increased separation especially in the fine particle range. Using this principle charged particles are deposited on oppositely electrostatic charged drops. An electrostatic nozzle scrubber for particle separation was developed. In this paper the charging system for both components and first results of grade efficiency measurements are presented.

Using vehicle-to-grid concepts to balance redispatch needs: A case study in Germany

The transition to more sustainable energy generation challenges transmission system operators to include intermittent renewable generation as well as electric vehicles into the power system. Especially in uniform-price power markets, this results in the need for unpopular grid expansions to overcome grid congestion. We analyze the ability of the expanded German transmission grid to cope with the increasing penetration of electric vehicles. We find that uncoordinated charging is likely to overload the transmission system and therefore propose a decentralized local market mechanism to include electric vehicles into the congestion management mechanism. We calculate redispatch needs and the associated possible remuneration of electric vehicle owners. We conclude that EVs can effectively reduce the need for redispatch and receive an according compensation.

Efficiently solving DSM problems: Are we there yet?

With an increasing amount of renewable energy generation, the scheme of supply following demand is no longer viable. As a consequence, aggregating entities (e.g., utilities, service providers) have to find new ways to balance demand and supply in order to guarantee an economic and environmental friendly operation of the energy grid. An approach recently extensively studied is the concept of duration-deadline jointly differentiated energy services that elicits temporal flexibility of the demand side. This paper considers different mathematical models that can be used to solve this demand side management problem applied to an electric vehicle charging use case. A classically applied approach (referred to as classic approach) uses a three-dimensional allocation matrix whereas a specially designed approach for this problem class (referred to as multiple deadline approach) uses majorization theory to answer the questions of adequacy and adequacy gap. These approaches are compared in regard to their time to create and time to solve the optimization problem as well as their sensitivity towards an increasing number of customer, deadline, and scenarios of renewable power generation. The results show that computation time of the classic approach is strongly influenced by the number of scenarios and customers whereas computation time of the multiple deadline approach is strongly influenced by the number of deadlines and scenarios. Neither of the approaches can be described as superior to the other as both react differently to input data. Furthermore, the results show that for a large-scale implementation both approaches must be improved in their complexity to ensure a continuous operation.

Efficiently solving DSM problems : Are we there yet? : A Real World EV Use Case [in press]

With an increasing amount of renewable energy generation, the scheme of supply following demand is no longer viable. As a consequence, aggregating entities (e.g., utilities, service providers) have to find new ways to balance demand and supply in order to guarantee an economic and environmental friendly operation of the energy grid. An approach recently extensively studied is the concept of duration-deadline jointly differentiated energy services that elicits temporal flexibility of the demand side. This paper considers different mathematical models that can be used to solve this demand side management problem applied to an electric vehicle charging use case. A classically applied approach (referred to as classic approach) uses a three-dimensional allocation matrix whereas a specially designed approach for this problem class (referred to as multiple deadline approach) uses majorization theory to answer the questions of adequacy and adequacy gap. These approaches are compared in regard to their time to create and time to solve the optimization problem as well as their sensitivity towards an increasing number of customer, deadline, and scenarios of renewable power generation. The results show that computation time of the classic approach is strongly influenced by the number of scenarios and customers whereas computation time of the multiple deadline approach is strongly influenced by the number of deadlines and scenarios. Neither of the approaches can be described as superior to the other as both react differently to input data. Furthermore, the results show that for a large-scale implementation both approaches must be improved in their complexity to ensure a continuous operation.