No abstract

Fast charging of electric vehicles (EV) is assumed to affect the operation of electricity grids. In order to decrease the need for conventional grid reinforcement, energy legislators are increasingly motivated to design load‐management incentives. Using the example of Germany, we investigate the extent to which voltage‐control strategies decrease critical voltage levels in suburban low‐voltage grids compared to uncontrolled charging. Voltage violations are identified by load flow simulation. Results showed that, for uncontrolled charging of 22 kW, each scenario except for EVs at the beginning of the line violated the voltage operation limit. Under these circumstances, the grid was operated within the voltage limits for 99.2 % of the year. Violations no longer occurred if active power limitation was performed. With reactive power control, full prevention of voltage‐limit violation could not be achieved but the frequency of violations was reduced. A combination of both active power‐limitation and reactive power reduced the volume and frequency of power limitation. In most cases, a power limitation of 2 kW satisfied the voltage boundaries. Thus, voltage control decreases the need for conventional grid reinforcement if grid operators gain more real‐time information about the state of their distribution grids.

In this work the impact of a high penetration of air to water heat pumps and PV plants on the distribution grid in residential areas is investigated. Results show that increasing PV penetration increases the hours of critical states in the distribution grid. Air to water heat pumps reduce those effects slightly when they are added to the grid. With an increasing penetration of heat pumps new problems, such as load peaks in the mornings, arise. By integrating voltage dependent droop control into the heat pumps, the negative effects on the distribution grid can be reduced. This reduction comes with a loss of HP efficiency and shows strong seasonal variability. For this study a set of representative grid layouts is used. Electric and thermal load profiles for each house are generated using the synPRO stochastic bottom-up model. The thermal load is covered by variable speed electric heat pumps combined with thermal storage. Resulting electric loads are used as input for a probabilistic load flow model

No abstract

The increasing prevalence of distributed photovoltaic (PV) units raises stress on distribution grids and necessitates increased grid planning efforts. We present a decision support system (DSS) based on integer programming that is able to determine cost-optimal grid reinforcements at the level of individual grid segments. The functionality of the DSS is demonstrated in a scenario analysis of a rising adoption of PV units relying on 1,000 simulation runs in a real-world grid. Based on the results, we provide guidelines for operative grid planning and illustrate how the system assists in the evaluation of reinforcement technologies as well as in long-term investment planning. Furthermore, thanks to segment-specific optimization, the DSS shows that at constant adoption levels, reinforcement cost can vary largely depending on the location of the PV units in the grid. Therefore, a high amount of uncertainty seems unavoidable in long-term prognoses on the effects of solar power on distribution grids