Network Decomposition

Conducting power flow studies on large-scale PNMs within a reasonable amount of time using a realistic non-linear AC model requires decreasing the solution space. A network decomposition approach is therefore implemented which divides a PNM into independent parts allowing the power flow problem to be individually solved for each part. Alternatively, the AC model can be approximated by a linear DC one trading in accuracy for performance. Either way, computational requirements only grow linearly with the size of the PNM not limiting the number of electrical consumers or voltage levels to a certain number. Power flow studies on city-scale PNMs can thus be conducted within seconds.

The network decomposition approach is not applicable to PNMs showing high redundancies as it is based on dividing a PNM into independent parts. The larger the individual parts the higher the computational requirements. In the PSP process, radial and ring networks are currently planned without connections between different areas. Since both types of topologies are mainly used in LV and MV real-world power systems showing only limited redundancies if any at all, generated PNMs quite, although not absolutely, accurately reproduce reality in this regard. Meshed networks, mostly found in HV grids, by definition already show redundancies. Since HV grids typically only comprise a couple of hundreds or thousands

of buses and branches, computational requirements are manageable. Allowing to freely set the level of redundancy according to the available computational resources to realistically account for redundancies existing in real-world power systems would certainly increase the attractiveness of the approach.

2.6.7 Scheduling of Battery Energy Storage

The scheduling approach optimizes charging/discharging of mobile or stationary battery energy storage by means of dynamic programming. It builds on a battery model allowing to realistically quantify battery aging costs and utilizes present and forecasted electricity prices to determine a charging/discharging schedule which is globally optimal given deterministic prices. In practice, however, several limitations restrict optimality:

• Battery aging model

The applied battery aging model defined in Section 2.4.1.3 is empirically derived for a specific cell type. Common to all empirically derived models is their limited expressiveness to precisely quantify battery aging costs for other cells of this type, much less for cells of other types. This is mainly due to deviations in the manufacturing process. Another reason are the many changes in the cycling regime of a PEV’s battery that cannot be accurately reflected in synthetic cycling tests. Based on an empirical model, continuous re-calibration during operation time is required by employing a battery state of health online-monitoring technology [108]. This way, a sound cost estimation depending on the current battery state including calendar aging which is so far neglected in the current model can be achieved.

• SOC operating range

Related to the applied battery aging model is the operating range of the SOC. The scheduling approach tends to keep the battery atSOC states around its optimal point of operation at 0.44 and moderate ∆SOCs. This way, extremeSOC states or deep cycling can be avoided both having a detrimental effect on the battery’s lifetime. Imposed restrictions on the charging/discharging power thus only have a negligible effect on the resulting profits. Similarly, keeping a moderateSOC buffer reserved for driving purposes of almost up to the battery’s optimal operation point only insignificantly reduce profits.

• Price information

Available price information for real-time electricity markets often include a forecast horizon of several hours. Those forecasts, however, are subject to uncertainties, usually the larger the more ahead they predict the future. The optimization process implicitly deals with those price uncertainties by means of the rolling horizon approach without explicitly learning from historical data. The latter approach would be a powerful extension since imperfect price information may distort optimality of the process and narrow down profits. Given price information exhibiting high accuracy as they are usually available in electricity markets for a couple of hours, the lookahead can have a significant positive effect on profits when increased within a range of several hours while any further increase does not lead to notable improvements as shown in [15].

• Dispatch probability

The optimization process assumes probabilities of 100 % on its determined power values neglecting possible power system constraints. For small-scale batteries as employed in PEVs or at home this assumption may be accurate in today’s power systems in the G2B case. An uncontrolled energy supply in the B2G case may destabilize the power system.

Energy dispatch therefore needs to be coordinated by a central instance resulting in probabilities of less than 100 %. Lacking dispatch probability data from any real-world power system, the influence of the made assumption remains an open question and needs to be further investigated.

2.7 Conclusions

In this chapter, a holistic modular PSS framework including a PSP and power flow simulation approach for generating and evaluating large-scale PNMs is presented. The methodology iteratively employs a combination of algorithms allowing to create PNMs with a great variety of different characteristics using minimal input data. The framework is able to generate PNMs bottom-up using consumer location and power demand data to particularly allow for a more realistic planning of the LV grid. With its versatility and ability to flexibly tailor the power grid to individual use cases, the framework overcomes the common problem of limited availability of information on real-world power systems. The analysis and comparison of different PNMs especially compared to real-world power systems is facilitated by defining various topological, electrical, and economic properties.

The PSS framework comprises an AC power flow simulation which is used to ensure that the generated PNMs are functional. Furthermore, it can be used to conduct studies simulating power flows under arbitrary tempo-spatial load profiles. The simulation calculates the capacity utilization at every bus and branch as well as the voltage at each consumer and substation which allows identifying times and locations of grid congestion and voltage drops.

The price-responsive energy storage scheduling approach considers battery degradation and electricity price forecasts to shift charging/discharging into time periods being effective with respect to maintaining power grid stability. It also ensures an economically profitable operation for the battery owner while complying with given constraints. This becomes handy in case of a large-scale integration of battery energy storage into the investigated power system to avoid further peak load increase when charging those batteries.

On the level of topological and electrical properties, PNMs generated by the framework match real-world power systems. The framework, however, comprises several limitations which can be ascribed to a lack of available input data. It misses to emulate the evolution of real-world power systems and neglects placement constraints and inhomogeneities of electrical installations on the same voltage level. While for some purposes PNMs may not require a more detailed modeling, for others the value of those PNMs may be limited. In the context of applying the framework on the example of Singapore in Section 4.2, the effect of limited input data in general and the mentioned limitations in particular on the explanatory power of the generated PNMs is discussed.

Content

3.1 Introduction . . . 48 3.2 High Level Architecture . . . 49 3.3 CityMoS Power . . . 60 3.4 CityMoS Traffic . . . 69 3.5 CityMoS Frontend . . . 71 3.6 Architecture and Interactions . . . 76 3.7 Discussion and Related Work . . . 82 3.8 Conclusions . . . 88

As a distributed simulation platform comprising a power and a transportation system simulation, CityMoS provides the infrastructure required for investigating the impact of different road transportation electrification scenarios on urban power systems.

3.1 Introduction

Answering this work’s research question of quantifying the impact of large-scale road trans-portation electrification on urban power systems requires a simulation platform, as anticipated in the proposed solution statement. Such a platform has to comprise at least a power and a transportation system simulation interoperating with each other. A detailed investigation requires both simulations to be modeled at the microscopic level considering the behavior of individual elements. For the transportation system these elements are the single vehicles while for the power system individual consumers, substations, power stations, and power lines need to be modeled. This allows exploring the power system impact of a great variety of different electrification scenarios, scheduling strategies, vehicle-to-grid (V2G) implementations, charging behaviors, and CS distributions. Considering both systems at the same time further allows optimizing charging infrastructures and scheduling strategies with regard to optimal satisfaction of the power demand and minimal power system impact. Other possible application scenarios include the investigation of the impact of distributed energy sources or the transition towards more intelligent consumers participating in demand response schemes. Also, scenarios for expanding power networks in developing countries under fast changing conditions can be explored.

The City Mobility Simulation (CityMoS), previously termed the Scalable Electric Mobility Simulation (SEMSim), described in this work is such a simulation platform allowing different, possibly distributed simulations [109] to interact with each other. In CityMoS, the specific interacting simulations are not stipulated but instead they depend on the individual use case allowing them to be freely combined. CityMoS as used in the context of this work comprises an agent-based transportation system simulation interoperating with a power system simulation using an open standard while at the same time offering the possibility to interactively and visually influence the simulation which is currently unique, especially with the offered functionality of the different platform components. In the following, the individual components of CityMoS used in this work are presented:

High Level Architecture (Section 3.2) is a standard for constructing reusable and interoper-able distributed system simulations enabling bidirectional communication among the different entities, even across heterogeneous hardware and software platforms. This section outlines the different components of the standard, discusses available implemen-tations, and provides an overview on the general process involving multiple interacting simulations.

CityMoS Power (Section 3.3) is an implementation of the PSS framework described in Chap-ter 2. Its purpose is planning and evaluating PNMs which realistically emulate an actual infrastructure as well as subsequently conducting power flow studies with different road transportation electrification scenarios on those PNMs. This section focuses on the software implementation part of the PSS framework as part of the CityMoS platform.

CityMoS Traffic (Section 3.4) is an agent-based transportation system simulation able to realistically simulate trips of a PEV population corresponding to the real vehicle pop-ulation of a city. In this section the different simpop-ulation components are described.

CityMoS Traffic is the only part of the CityMoS platform which was not developed in

the context of this work but instead simply used as an externally prepared simulation component.

CityMoS Frontend (Section 3.5) is an interactive visualization tool allowing to participate in and controlling of distributed simulations using the High Level Architecture. The user is thereby enabled to simultaneously interact with multiple simulations and visually inspect their output at runtime. In addition to possible interactions occurring in the context of the CityMoS platform, its main functionality with respect to both visualizing data and providing user interactivity is described in this section.

The remainder of this chapter is concerned with explaining the platform’s architecture, the specific requirements for each simulation to participate in a distributed simulation, and the exchanged interactions in Section 3.6. This description is followed by a discussion of alterna-tive ways of constructing distributed system simulations, experiences with the implemented standard, as well as the CityMoS platform in general and alternatives to its power and transportation system simulation in Section 3.7. Section 3.8 concludes this chapter.