Simulation Tools

To evaluate the impact that different control strategies have within the previously described microgrids and multi-microgrids architectures, it is important to analyze the electric system behavior in steady-state and assess the different transients and dynamics responses. Similarly, it is also important to evaluate the impact that communications systems have, namely due to data losses and delays, in the overall performance of different control strategies envisaged for the distribution network.

The major advantage of using simulation tools is the flexibility in reconfiguring and testing different scenarios, solutions and approaches. It allows the evaluation of the electric and communications networks separately or together.

2.11.1 Simulation of Electric Networks

Among the different available simulation tools for power systems the most reputed are PSS/E⁹ from Siemens and Eurostag¹⁰ from Tractebel. They are both commercial licensed software versions commonly used in the industry and in academia for technical research, studies and operational assessment of power systems. They incorporate several models for generators (synchronous/asynchronous machines, power converters, etc), transmission lines and loads. They both provide dynamic and static models and allow specific user models to be implemented. It is possible to run in both scripts allow an automated con-figuration of the electric power systems conditions and potentially allow the interaction with external tools.

2.11.2 Simulation of Communications Networks

Network simulation represents one of the evaluation methodologies for development and testing of commu-nications architectures, protocols, technologies and other related topics. The majority of commucommu-nications network simulators are event-driven. Events are triggered by node activity and the simulator platform defines a list of scheduled events that are processed as the simulation progresses throughout a predefined time interval.

There are a considerable amount of network simulators currently available, which are able to imple-ment a wide variety of communications scenarios and technologies. Overall, they can be divided into industrial/commercial and academic, targeting different users and applications. Given that academic implementations of these simulators have generally no licensing costs and typically provide the source code for open contributions they are deemed as advantageous when compared to commercial solutions.

There are several different characteristics sought in communications simulators and the following can be considered to decide over the appropriate solution:

9 “PSS/E” - http://www.energy.siemens.com/hq/en/services/power-transmission-distribution/power-technologies-international/software-solutions/pss-e.htm

10“Eurostag” - http://www.eurostag.be

ˆ Modularity - the source code should take advantage of enhanced features of modern programming languages such as Java, Python or C/C++ like the modular object-based modeling approach;

ˆ Scalability - the increase of the number of nodes or the amount of exchanged data in a commu-nication network to be simulated should be implemented without compromising performance or significantly impacting the development time;

ˆ Performance - the considerable variation of communicating nodes when different scenarios are con-sidered, require that the simulation platform is capable to withstand different numbers of nodes while mitigating the increase in computational effort, e.g., memory usage;

ˆ Documentation - the available simulator documentation and its quality can a considerable impact in the learning curve, allowing a quick development of communications scenario and to tackle the features of the simulation tool in a reasonable time;

ˆ Support - like documentation, support is a key issue since it provides a helpful resource in the development process.

In [90] a complete comparison between the most recent discrete simulators is presented and a bench-mark procedure is implemented using a reference network topology for performance assessment. An initial square topology with 16 nodes was used with only one sending node generating a data packet every second. The packet is broadcasted to neighbor nodes which will relay it, adding an extra second delay to emulate local processing, up until the destination node is reached. The performance evaluation was conducted on the following network simulators: ns-2¹¹, ns-3¹², OMNeT++¹³, JiST¹⁴and SimPy¹⁵. The results regarding the simulator performance when considering different network sizes, can be found in [90] and are presented also in Fig. 2.20.

Results show that overall both ns-3 and OMNeT++ present the best alternatives in terms of network size, which is envisaged of considerable importance since the communications systems to be simulated in smart grid scenarios can have a significant amount of communicating nodes and various forms of implementation.

2.11.3 Integrated Simulation

The combination of electric and communications simulators has been a subject of interest and it has been explored under an integrated simulation scheme, often designated by co-simulation, in the sense of cooperative simulation, or integrated simulation. This topic is not actually new and it has been more intensively researched in the last years. These integrated systems intended initially to combine simulators that were originally conceived to work separately, as such, the main challenge was how to integrate them. The fact that communications simulators are event driven, meaning a discrete processing strategy is used; however electric simulators intend to represent electromagnetic and electromechanic,

11“ns-2” - http://www.isi.edu/nsnam/ns

12“ns-3” - http://www.nsnam.org

13“OMNeT++” - http://www.omnetpp.org

14“JiST” - http://jist.ece.cornell.edu

15“SimPy” - http://simpy.sourceforge.net

Figure 2.20: Scalability Performance of Simulators

meaning a continuous processing strategy. This can create a mismatch when integrating power systems and communications networks simulators.

One of the first integrated simulating platforms was the EPOCHS framework [91] that uses a fed-eration of simulators: PSCAD/EMTDC for modeling electromagnetic transients, PSLF for modeling electromechanical transients and ns-2 for simulating the communications network. The simulators are managed though the Runtime Infrastructure software module (RTI) that periodically allows them to exchange information thus enabling a synchronization between both processes. It uses an agent based platform for monitoring and control actions in the electric network. The agents are able to exchange data among them.

Since power systems simulators commonly use numerical algorithms to solve differential-algebraic equations that model the system dynamics [92]. This means that the power system simulation can be considered also as a discrete. Hence time step is used, typically small to prevent abrupt changes, to evaluate the system variables behavior. In [92] the Discrete Event System Specification (DEVS) formalism that was introduced to enable the hybrid simulation of electric and communications systems, through means of synchronization of events. Despite the fact that ns-2 is used for simulating communications networks, the electric power system had to be modeled through mathematical equations.

Another co-simulation platform is the PowerNet introduced in [93], which combines Modelica for the power system and ns-2 for the communications. Different synchronization strategies are approached and the selected one was to enslave Modelica to ns-2, in order for the latter to determine all time instants where data exchange between them occurs. The interaction is performed through read/write functions and in terms of simulation time ns-2 is always ahead of Modelica.

A Global Event-Driven Co-Simulation Framework (GECO) is proposed in [94] that also aims, like EPOCHS, to combine power systems and communications networks simulator. The same combination of PSLF and ns-2 is proposed by the authors, but a global event scheduler is proposed. This scheduler defined in GECO avoids the errors introduced by the time-stepped approach of EPOCHS for instance, since power systems and communications simulators are discrete. As such a global event queue is used

where the events from the communications simulator are interleaved with the events from the power systems simulators according to their timestamps. The combination of these events is validated through the DEVS formalism. The co-simulation is driven by a sub-component in ns-2. In [94] a comparison between co-simulation platforms, some of them mentioned earlier, is also presented.

Other similar implementations worth mentioning are [95] and [96]. In the first case a combination of ns-2 for the communications and OpenDSS for the electric network is proposed. A real feeder is modeled and a wireless IEEE 802.11 communications network is used to support monitor and control activities.

In the second a combination of MATLAB and OMNET++ is used, respectively for the power system and communications. A models with an application and a middleware layers were designed to interact with a support layer, being the latter responsible for interacting with both simulators. In both cases the information regarding the implementation and the synchronization mechanisms used to couple the proposed co-simulation framework is limited.