Design of Distributed Energy Systems

Design of Distributed Energy Systems Role and Requirements of Modeling and Simulation

F. Felgner, J. Meiers, L. Exel, G. Frey Chair of Automation / Dept. of Mechatronic Engineering Saarland University Saarbrücken, Germany {felix.felgner; josef.meiers; lukas.exel; georg.frey}@aut.uni-saarland.de Abstract—The interconnection of distributed generation units has a significant impact on the performance of an energy system. In this paper, challenges in designing grid-connected and islanded distributed energy systems (DES) are described and a concept of simulation models in the component-oriented modeling language Modelica® is presented, wherein simulation models are divided into physically detailed and physically abstracted models.

High economic expense in fuel transport and system operation: Up to now, diesel generators are often the primary or unique source of electricity at locations without sufficient infrastructure. An example: According to estimates by the U.S. Department of Defense, fuel delivery costs to remote military bases are up to 60 € per liter [2]. In addition to the high fuel delivery and maintenance costs, there is a significant risk to the soldiers protecting those convoys.

Keywords—modeling; simulation; distributed generation; distributed energy systems, renewable energy; Modelica

High noise pollution by diesel generators: Continuous operation of fuel driven generators emit a permanent and high noise level that causes additional mental stress to people in their vicinity.

I. INTRODUCTION Energy production from renewable energy sources (RES) is gaining increasing importance due to being one of the world’s fastest-growing energy source, increasing by 2.5 percent per year [1]. As an alternative to energy generation from conventional energy sources such as diesel, natural gas, and coal, it protects the environment by reducing greenhouse gas emissions. There is sufficient supply of clean wind and solar energy, which are the most frequently used RES. Due to their advantages of long lifetime and low maintenance requirements, photovoltaic (PV) systems and wind generators can be cost effective. It must be recognized that the generated power from these RES is intermittent and depends on seasonal, local, and short-time fluctuations. For this reason, the prediction of energy generation based on methods of supply forecasting for the specific location and time period has to be done in the first planning phase. A. Applications Considered With the use of RES new challenges will emerge that have to be investigated in order to establish a reliable and flexible distributed energy system (DES) with a wide area of applications (an exemplary DES with typical components is drawn in Fig. 2): Besides (semi-)self-sustaining energy systems of buildings, real estates, and districts, our considerations also apply to, e.g., emergency aid and refugee camps as well as remote military bases. Even in the latter cases, RES are gaining importance as a consequence of several aspects:

One main issue with the integration of RES is the fact that energy generation is not possible by a defined schedule, i.e. energy has to be consumed when it is generated. Predictive Feed-in Model

Physical part of DES Energy Consumer Units Electrical

Energy Generation Units Electrical

Thermal

Thermal

Energy Storage Units Electrical

Thermal

Energy Management System Fig. 1. Concept of DES operation: main parts and interactions.

Taking this constraint into account, besides the existing energy generation units and loads, the following sub-systems are needed (see Fig.1): energy storage systems (ESS), predictive generation and load models, intelligent energy management systems (EMS) including monitoring methods that ensure reliable failure detection.

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Predictive Load Model

B. Contribution and Organization of the Paper A promising method to analyze the DES behavior under given constraints is modeling and simulation. It is seen as a useful means from the early design phase to the operation under predictive control—though requiring differently constructed models depending on the questions to be answered. This paper addresses the specific problems in DES which contain a high percentage of RES. We identify general issues occurring with the integration of RES (sec. II). Furthermore, we propose an approach of DES modeling and simulation with the component-oriented modeling language Modelica®, wherein models are divided into physicallydetailed and physically-abstracted models (sec. III). Finally, summarizing conclusions and an outlook are given (sec. IV). II. INTEGRATION OF RES

 Configurability and extensibility. Requirements on EMS can change during operation due to modified user behavior or aging processes in system components that have to be respected by configurable controllers. B. Feed-in and Load-Forecast Models Feed-in forecasts of supply dependent energy sources as well as the forecasts of non-controllable loads play a key role in the design and operation of a DES in an efficient and reliable way. Forecast models can be developed by a statistical approach on the basis of longtime recorded data that identify correlations between the system inputs and outputs and include autoregressive models, artificial neural networks, fuzzy systems and support vector machines. However, the requirements on forecast models providing forecast time series fundamentally differ depending on whether they are intended for DES design or for DES operation:

A. Challenges of DES Operation The prediction of energy flows in distributed feed-in energy systems with RES can only be done with unavoidable uncertainty. As a result, energy has to be consumed or stored when it is generated and efficient and low-loss distribution of energy flows in DES with ESS, energy consuming and generating devices has to be done by intelligent control in order to offer the following functionalities:

 Forecast models used for design have to be qualitatively correct. This means that the forecasted time series has to be typical of the respective location in terms of chronological sequence, fluctuation, and mean supply or demand value, respectively. As a consequence, one of the easiest and best approaches is to use historical data for a specific site if data are available.

 Control of active and reactive power. In AC systems, besides active power, reactive power has to be taken into consideration, due to the fact that it can lead to instability of the voltage profile and additionally burdens the grid depending on connected devices [4].

 In contrast, forecast models used for operation have to be quantitatively and chronologically precise; they shall provide reliable values for future periods of time.

 Detecting of imbalances and counteracting. Technical devices are designed for a certain operation range, where they work reliable, without errors and most efficiently. Deviations from this range have to be detected by measuring and corrected adequately.  Detecting and enable switching of operation modes at any time (e.g., where applicable, grid-connected and islanded operation mode). Legal requirements concerning the tolerance of voltage frequency and level are more strict in grid-connected mode as in island mode to ensure the stability of grid [3].  Enabling import and export of electrical energy from and to the connected grid. In cases when there is not sufficient generated and stored energy in the system, it has to be imported from the grid. Conversely, energy can be given to the grid, if generation exceeds consumption und storage level.  Ensuring maximal lifetime and efficiency of system components by an optimized operation mode. Operation behavior of system devices such as charging and discharging characteristics of ESS have a significant influence on their efficiency and lifetime [5]. For instance, it is known that storage capacity of ESS will be reduced with rising number of charging cycles [6], [7].

III. MODELING METHODS AND ABSTRACTION LEVELS REQUIRED FOR DES A. General Requirements For DES design and analysis, an executable system model is required which respects the physical DES components contained: energy sources, sinks, converters, transmission lines (if applicable), and storages. As an example, consider the DES depicted in Fig. 2. In order to realistically capture DES behavior under individual local conditions, the model has to be extendible by respective data-based weather models including the fluctuating renewable energy supply (cf. sec. II). DES simulation results are needed on very different time scales, depending on the dynamic processes which are of interest or relevant for the respective behavior. They range (cf., e.g., [8])  from near-term behavior (on a scale from milliseconds to several seconds, respecting the fast electrical and, if applicable, mechanical transients—required to analyze/validate system stability immediately after switching operations or quick changes of state caused by control or user actions)  over medium-term behavior (from an hour to several days; for numerical efficiency reasons, only with applicable moderate, e.g. thermal, transients—required to analyze/validate controlled DES operation subject to

intraday fluctuations of renewable energy supply and market prices)

hybrid Modelica® system and component models are presented in [14].

 to long-term behavior (one month up to years, with appropriate transients—required, e.g., to decide on the cost-effectiveness of a planned DES).

The Modelica® models and simulation results shown in this paper have been edited and computed by the commercial software Dymola® [15]; a list of other, in part freely available, Modelica® tools (with differing capabilities in detail) are listed in [16].

Regarding the models applied to medium- and long-term simulations, the following has to be noted: Any negligence or temporal averaging of transients from a shorter time scale presumes that they do not have an enduring effect on the system. (This is not self-evident, for instance, w. r. t. stability issues or the amounts of renewable energy to be temporarily stored.) When modeling a DES, systems from different physical and technical domains have to be considered: in the first place, there are electrical and thermal components, if applicable, also mechanical components; in addition, every modern DES comprises components of control—from the continuous as well as from the discrete-event domain. Such a system behavior is most efficiently captured by a modeling/simulation scheme with corresponding capabilities: it should provide  cross-domain applicability,  component-oriented modeling (for a clear model structure and re-usability of component models), and  the ability to deal with hybrid dynamics. B. Component-Oriented DES Modeling with Modelica® The non-proprietary, multi-purpose modeling language Modelica® [9], [10] is a suitable means that has been conceived for multi-domain systems. Its component-oriented concept is based on ordinary differential-algebraic equations (for continuous-time behavior) as well as algorithms (for discrete-event behavior). The connector variable of equationbased component models can be acausal, i.e., they may represent those physical quantities by which two connected components interact in a bidirectional way (i.e., if the connector represents, e.g., an electrical pin, its variables may be the electrical potential and the current). Thereby, Modelica® models can directly reproduce the physical DES structure with all its components and sub-component. Consequently, these models fundamentally differ from classical signal-oriented models, e.g. in Matlab®/Simulink® [11], which have to reflect the mathematical system structure 1. There is a large variety of free and several commercially licensed Modelica® libraries from many application domains, containing reusable component models that have been developed by Modelica® practitioners from all over the world [13]. They include the electrical, thermal, and mechanical domains. ®

Generally, Modelica also supports hybrid dynamics. Elaborate examinations as well as modeling schemes for

1 This is not true for the commercial Matlab® toolbox SimscapeTM [12], which follows a similar acausal concept as Modelica®.

C. Levels of Detailing in Physical DES Models In the design phase of an individual DES, we propose to work with models of two different detailing/abstraction levels, depending on the respective question to be answered: 1) Physically-Detailed Models (PDM) Physically-detailed models (PDM) fully exploit the possibilities of component-oriented physical modeling (cf. sec. III.B). They are required for the analysis of single components, their bidirectional interaction, and the dynamic near-term behavior (cf. sec. III.A). PDM have been used in previous projects, e.g., for an Organic Rankine Cycle plant [17], for thermoelectric generators [18], for an economic comparison of concepts [19], and, in general, for many thermo-power systems during the recent decade, e.g. in [20], [21], [22]. And there exist components models in the free standard library “Modelica” [13], applicable to general electrical, mechanical, and thermo-hydraulic systems, and, specifically for electric power systems, the complex library “PowerSystems” (licensed) [13], [23]. However, the application of PDM should be restricted to those problems and components models where their physical preciseness is actually essential. Even with substantial application of component models from basically ready-to-use libraries, there remains considerable effort to individually parameterize and/or adapt component models—which often exceeds the specifications given in datasheets—and to develop missing component models with high level of detail. Last but not least, medium- and long-term simulations of the entire DES with physically-detailed models are very time- and memory-consuming if steady-state or slow (e.g. thermal) processes are superposed by high frequent (e.g. electrical) ones. 2) Physically-Abstracted Models (PAM) In order to address problems for which PDM are not efficient, we propose physically-abstracted models (PAM). They should still be component-oriented, but unlike PDM, especially those from all-purpose libraries, PAM have to be  specifically conceived for (re-)use in DES design;  based on abstractions of the internal relationships that have to match the specific purpose of the models,  computationally efficient. Firstly, PAM are intended for the analysis of medium- and long-term DES behavior. Secondly, PAM, supplemented by RES models (cf. sec. II), can be an efficient means in an early design phase of a DES in order to find the appropriate elementary architecture and the approximate dimensions of components. The premise of the PAM concept is that these

design decisions can be made on the basis of fundamental (quasi-) steady-state scenarios and sequences of scenarios. For this purpose, PAM of physical DES components (cf. III.A) should focus on a correct reproduction of the  energy and material flows (absorbed and emitted through the connectors)  under nominal operational conditions, usually defined in component data sheets. In addition, a DES design scheme should be able to consider cost effectiveness as well as environmental targets. To this end, we suggest that PAM be extended by appropriate information transmitted through the connectors:  monetary and environmental cost flows, which are assigned to the energy and material flows (e.g. primary costs, CO2 and other harmful emissions). On the component model level, the PAM concept (partly) abolishes the acausal concept of the PDM, whereas on the system level, it can still be used in a beneficial way: In the Modelica® model in Fig. 2, the connection of PAM components forms a DES network of electrical energy, hot water, and (natural/bio) gas flows (blue, red, and green lines). The connections imply an even balance in all of the corresponding nodes at any time. Thereby, a simulation of this intuitively constructible model automatically delivers the deficits and surpluses of the three flows in the connectors of the components on the right-most side („compensator_Elec“, „compensator_HotFluid“, and „compensator_Gas). Alternatively, causalities can be reversed, e.g. if the connector flows of „compensator_Elec“ are fixed (to zero in isolated operation) and the flow from/to “storage_Elec” is left variable instead. In Fig. 3, a quick year-round simulation of the electrical part of the DES model (see grey dotted frame in Fig. 2) is used to find size and initial charge of an electrical strorage required to compensate temporary deficits of solar power under the irradiation conditions measured at a specific location in the year 2012. IV. CONCLUSIONS AND OUTLOOK In distributed energy systems (DES), several challenges originate from an extensive integration of renewable energy sources (RES) as well as from individually varying applications, sizes, and environmental conditions. While the key role of modeling and simulation in general—as an effective means for system design—already seems selfevident, the paper brings out particular requirements of DES models, which, in part, depend on the respective issue addressed. Among others, we recommend two abstraction levels for the modeling of the physical DES components: physically-detailed models (PDM) and physically-abstracted models (PAM). While a good part of PDM components required may be re-used from existing Modelica® libraries, we have been making new efforts for the PAM. A cooperative project on a class of real islanded DES has already been defined at the Chair of Automation (Saarland University, Germany). Besides PDM and PAM for the design

phase, appropriate predictive models will have to be developed for online operation under optimal control. REFERENCES [1] [2]

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Fig. 2. DES model diagram using physically-abstracted model (PAM) components, implemented in Modelica®, edited with Dymola® 2014.

t [days]

t  0 qE dτ [energy units]

Sizing of a storage to compensate temporary supply deficits

Capacity

b.

Initial charge

Energy flow qE into “compensator_Elec” [power units]

Deficit | Surplus

Deficit | Surplus

a.

t [days]

Fig. 3. Consider part of DES model from Fig. 2 with grey dotted frame: Simulation with solar irradiation data over 1 year (measured at University of Kaiserslautern, Germany, www.eit.uni-kl.de/atplus/, in 2012); instantaneous (a.) and total (b.) deficit and surplus of solar-electrical power; sizing of a storage required according to data.