1. No category

EnviroInfo 2013: Environmental Informatics and Renewable Energies
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
Towards Modular Assembling of Virtual Power Plant Control
Systems – The Smart Power Hamburg Platform
Jan Sudeikat, Onnen Heitmann1
Abstract
The large-scale integration of volatile, renewable energy sources requires means for the flexible operation of controllable energy resources. Consequently, Decentralized Energy Resources (DER) are pooled as Virtual Power Plants
(VPPs), i.e. sets of generation units which can be utilized as autonomous assets in energy generation portfolios. In
next generation VPPs, also storage and consumption units have to be integrated to increase the degrees of freedom
for the power generation scheduling. Since the generation, storage or consumption potentials of individual units underlie local constraints, the coherent and adaptive control of the VPP is an interesting technological challenge.
In this paper, we describe work in progress towards the construction of an extensible software platform for controlling VPPs. Within the research project Smart Power Hamburg, requirements on these software systems are examined
and an architecture is devised that allows to assemble control systems for differing pool configurations and business
objectives. We describe the requirements for such a software platform from a practitioner’s perspective and motivate
the guiding idea of a modular construction system for VPP control systems. In addition, an architectural approach is
outlined, which combines service- and agent-oriented design approaches.
1.
Introduction
The energy turnaround2 effects fundamental changes the ways how electricity is generated, transported
and traded. A major concern is balancing the fluctuating output of volatile energy sources, e.g. wind power stations and solar power plants. This requires means for the flexible control of the power generation.
Besides using conventional, large-scale power plants, the concept to cluster Decentralized Energy Resources (DER) for flexible control has been coined Virtual Power Plant (VPP).
The guiding idea is to orchestrate sets of decentralized, small-sized generation units in order to utilize
them as an aggregate generation facility. The technologic challenges for these systems are manifold.
Amongst others, major aspects are the handling of heterogeneous sets of DER and associated telecontrol
protocols (Pedersen et al 2010), the coherent information models for system interoperability (Uslar et al
2005), the optimized scheduling of generation units (Tröschel/Appelrath 2009) as well as security concerns (Kostyk/Herkert 2012). From a software technologic viewpoint, VPPs are interesting subjects. The
operation of individual units, which are constricted by their on-site context, have to be balanced with the
overall performance of the cluster.
In the joint research project Smart Power Hamburg (SPH),3 the constructions of next generation VPPs
is addressed. Business cases for VPPs and requirements for the software support are examined in parallel.
Next generation VPPs will not only combine generation, but also storage and consumption units, in order
to enhance the degrees of freedom in the utilization of the system. This heterogeneity demands increased
levels of intelligent control. Each unit is embedded in a context and has its own constraints and invariants,
which need to be respected by the operation of the cluster. Thus an agent-oriented design perspective is
1
Hamburg Energie GmbH, Billhorner Deich 2, D-20539 Hamburg, www.hamburgenergie.de,
email: {Jan.Sudeikat | Onnen.Heitmann}@hamburgenergie.de
2
In German: Energiewende
3
www.smartpowerhamburg.de
adopted (Ramchurn et al 2012, Singh/Huhns 2005). In this paper, we discuss the requirements on VPP
control systems and outline an appropriate architectural framework. A key design criterion is the extensibility of the control system. While the regulatory framework of the energy domain is continuously adjusted, new business opportunities arise and existing VPP compositions are to be adjusted. First, the framework provides extensibility, based on a service-oriented application design (Singh/Huhns 2005). Secondly,
VPPs require different levels of adaptivity. VPPs operate in volatile environments, where the business
goals (strategy level) and the operation contexts of individual units (operational level) can change.
This paper is structured as follows. In the next section, applications for VPPs are outlined. Requirements on the control systems are given in Section 3. Afterwards, our architectural approach is presented
(Section 4). In Section 5, related work is discussed before conclusions and prospects for future work are
given.
2.
Application Scenarios for Virtual Power Plants
Enabling flexible energy generation policies by implementing VPPs is a strategic aim for today’s energy
utility companies. The coordination of DER utilization is regarded as a means for ensuring the reliability
of the power distribution system and the systematic integration of volatile energy sources (e.g. see Microsoft Worldwide Power & Utilities Group 2013). Essential determining factors for actual business cases
are the national regulatory frameworks. Besides strategic objectives, the provision of control reserve4 is an
immediate example application scenario for VPPs in Germany (Albersmann et al 2012).
Control reserve is a system-level service used by the Transmission System Operators (TSOs) to maintain the nominal power frequency. This frequency within the power grid is directly affected by imbalances
between power feed-in and consumption. When the consumption exceeds the generation, additional energy has to be supplied. This so-called positive control reserve is invoked by increasing generation or decreasing the consumption. Vice versa, when the generation exceeds the consumption, redundant energy
has to be withdrawn for the sake of maintaining the integrity of the electricity distribution (so-called negative control reserve). Potential measures are the decrease of power generation and the increase of power
consumption.
According to regulations of the Union for the Co-ordination of Transmission of Electricity (UCTE)
three types of control reserves exist (UCTE 2004) and are traded in regular auctions.5 The primary control
is automatically invoked based on the decentralized observation of the current frequency. This service is
provided by power plant operators which regulate, with a pre-defined progression, their electricity output.
These adjustments stabilize deviations of the frequency. The secondary and tertiary controls are invoked
by control systems of the individual TSOs. These types of energy reserves can also be provided by VPPs.6
Major differences between these two types of controls are the response times. Secondary control reserve
has to be provided within 5 Minutes upon request and is traded on a weekly basis. Thus, the energy providers maintains always on connections to the TSO, which transmits control signals in a regular interval.
The tertiary control has to be provided within 15 minutes and is daily auctioned as individual products of
positive / negative reserves for 4 hour time slots. In Germany, requests for tertiary control power are
communicated to the VPP control by a custom request format, based on XML files transmitted via SFTP.7
Clustering distributed units for providing control reserves is explicitly permitted and detailed specifications of the expected behaviour of these VPPs are available.8 These specifications include the timely be4
In German: Regelenergie
www.regelleistung.net
6
detailed technical specifications can be found at: https://www.regelleistung.net/ip/action/static/prequal
7
https://www.mols-anbieter-client.net/ (German)
8
E.g. for Germany at: https://www.regelleistung.net/ip/action/static/prequal
5
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
haviour of the aggregate power generation / consumption and the communication connections which are to
be maintained to the TSOs.
3.
Requirements on the Software Support
The realization of innovative operational concepts for DERs requires an extensive automation and software support. Within our research project, the requirements for these software systems are studied and
field-tested. We aim at an innovative software concept, which enables the parallel operation of virtual
power plants in a coherent execution environment. The functional requirements on the VPP-control derive
directly from the business objectives, i.e. the regulations for the provision of energies for specific markets
(e.g. see Section 2). These specifications also indirectly define quality aspects9 which have to be met for
market participation, in particular reliability and efficiency aspects. Using the example of control reserves,
specifications define the observable, timely behaviour of the aggregated energy potentials as well as the
requirements for availability. In addition, numerous quality aspects are relevant, amongst others security
issues, the interoperability with regard to telecontrol protocols, the usability of control stations, as well as
the maintainability and changeability of the VPP control, e.g. due to changing business cases and market
conditions.
VPP control systems are intermediate software system elements that mediate between the detailed control of remote units and the generation scheduling of aggregate energy volumes. Key to the integration is
to abstract the internal workings of the VPP and equipping the system with autonomous control.
Facing increasing numbers of DERs, the efficient parallel operation of differing VPP configurations and
the timely adaptation of VPPs due to changes in regulatory frameworks and market conditions gain importance as an architectonic challenge. The parallel operation of isolated, proprietary software systems,
which are tailored towards specific business cases or DER types, facilitate market entrances. However,
this approach is not attractive in the long term, due to the implied overhead, e.g. for maintenance and infrastructure. We aim at a software platform which allows the composition of control system instances.
VPPs are organized as groups of hardware units which operate in parallel (see Figure 1, I). A key concept
for the differentiation of the control algorithms is the definition of an Energy Product. In this context, we
refer to these products as potential energy volumes, which can be provided by the coaction of the elements
under control within a single VPP. Products describe the energy generation, consumption, or mixture of
these within a given amount of time. From a software perspective these products map to time-tables of aggregate energy generation / consumption. Planning these products (Potential Schedules) and controlling
their provision (Schedule Commitments) is the main purpose of VPPs. E.g. Bitsch et al (2001) describe
timely profiles of energy supply offers as well as supply agreements as the sole interface between the
VPPs control systems and Energy Management Systems.
Consequently, operators of VPPs require means to adjust the internal working of the VPP control. Thus
the platform has to provide for the composition of functionalities, i.e. a modular construction system,
where the control system can be assembled based on reusable services (Singh/Huhns 2005). The interplay
of services implements individual control system configurations. The minimal set of functionalities required for a single VPPs are denoted in Figure 2 (II). Each unit and each cluster of units have to be
equipped with Customized Pool & Cluster Control algorithms. For homogenous VPPs aggregate algorithms may be sufficient, but orchestrating heterogeneous VPPs is intended. Due to the heterogeneity of
units and their associated communication protocols, a coherent data model, internal to the software platform is needed (Data Model & Archiving). Closely related are means to visualize the obtained data and
allow the manual remote control (Visualization & User Interaction). For VPPs additional user interfaces
are needed, which provide aggregate controls for sets of units. In Addition, means to interact with remote
9
E.g. according to DIN/ISO 9126.
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
DERs are required (Remote Unit Telecontrol). Interfaces are required to communicate with individual
units using standard Supervisory Control and Data Acquisition (SCADA) protocols and data models (e.g.
Clarke et al 2004). Sensor input and control decisions are to be encoded and archived. Control flows are to
be communicated among the service providers (Communication Infrastructure). Besides these basic functional components, the extensibility needs to be prepared. Potential extension points are supportive functions, e.g. forecasting mechanisms and planning components which are sourced out.
Figure 1
Parallel operation of VPP control systems in the software platform (I) and the composition of these control
system by assembling reusable functionalities (II).
VPPs operate in volatile operation environments, thus adaptive modes of operation are necessary.
Communications links can fail and the operation contexts of individual units can change, e.g. when (1)
wind power generation fluctuates, (2) the forecasted heat demand for a Combined Heat and Power (CHP)
unit deviates, or (3) technical failures prohibit the planned operation mode. In order to accommodate for
these uncertainties, the operational reliability has to be ensured by continuous monitoring of the execution
state of the hard and software components of the VPP and equipping software components with problem
solving abilities or handling exceptional execution conditions. In consequence, this leads to an agentoriented design approach (Sterling/Taveter 2009, Ramchurn et al 2012). Here, we understand agents as
pro-active software components, which monitor their internal state as well as the state of their immediate
environment, in order to infer courses of action (see McArthur 2007 for a discussion of agent definitions).
VPP designs benefit from adopting an agent-oriented design stance, as it enforces a distributed system
structure with inherent failure handling abilities.
4.
The Smart Power Platform – An Architectural Blueprint
In the following sections, we outline an architectural blueprint for VPP control systems. Following a layered approach (see Section 4.1), telecontrol techniques are reused (bottom-most layer) and interfaces to
trading and optimization applications are defined (top-most layer). The control system design mediates between these abstraction levels. The control system establishes an overlay to conventional telecontrol and
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
SCADA systems in order to prepare the agent-based operation of distributed units. A design rationale is
that agent-oriented control is to be integrated gradually. Simple, reactive agent models can be used to
structure the application design and are sufficient for the autonomous control of active power generation,
e.g. with regard to control reserves (see Section 2). When acceptance of the control paradigm is established, more sophisticated agent models, using deliberation and learning techniques, can be conceived. In
addition, we propose an architectural approach to integrating reliability and adaptability features (see Section 4.2). Based on previous works on adaptive system designs, we propose to enhance component designs, here service providers and agents, with means for internal observation and run-time modification.
Embedding distributed adaptation strategies among the elements of the control system allows for ensuring
invariants and restore operational parameters at run-time (Sudeikat et al 2012).
4.1
Hierarchical Control of Agent Autonomy
The organization of the functional components follows a layered structure (cf. Figure 2). The lowermost
Unit Layer contains the decentralized production, storage and consumption facilities. Examples are CHP
units, Heat storages (including heating networks), and Demand Side Management systems for real estates
or industrial processes. Via telecontrol protocols and encrypted communication links, these units are
steered. The communication comprises the transport of measured values and event signals, which are generated by the local control, e.g. realized on-site by a Programmable Logic Controller (PLC). In addition,
control signals are forwarded to the units. Due to the heterogeneity of the units and their associated communication protocols (e.g. Clarke et al 2004), the so-called Access Layer translates the incoming and outgoing messages. Inside the VPP control platform, a coherent semantic data model is used. A logical addressing scheme ensures the location transparency of internal data. Unit Gateways contain adapters for the
specific communication protocols, redundant connections are statically allocated.
The interplay within the Pool Layer realizes the VPP control logic. Each entity in the Unit Layer is controlled by a Unit Agent (1:1 relation). Concerning the utilization of a unit in the context of a VPP, this
agent is the sole access point - it operates an individual unit and controls unit commitments. The design of
these components follows an agent-oriented design (Singh/Huhns 2005), because they do not only provide
services, but also actively monitor the state of their associated unit and the units’ context in order to decide
proactively for courses of actions. Agents are associated at run-time to a Pool Control (n:1 relation), i.e. a
reactive component that is aware of different Energy Products and contains the logic how to plan for their
provision. For example, the ability to provide negative control reserves by decreasing generation (see Section 2) requires that a sufficient number of DERs are active during the committed time slots. In our current
development, pools are used to aggregate units of comparable technical abilities and thus are statically associated. In future stages, the allocation of agents to pools is expected to be dynamic, e.g. via coalition
formation techniques (e.g. Robu et al 2012).The main responsibilities of the Pool Control are the resource
scheduling in advance and in immediate reaction to events (Tröschel/Appelrath 2009), e.g. sudden demands for control reserves (cf. Section 2). In addition, the associated units are continuously monitored to
validate commitments.
The topmost Product Layer aggregates and presents the product types, which are made available by the
individual pools. These products are potential power volumes in predefined time intervals. Examples are
the provision of Tertiary Control Reserves (TCR), Secondary Control Reserves (SCR), the utilization of
units according to the German Renewables Energy Law (REL), and the trading of generated energy. Besides the manual querying of Energy Products, this layer may comprise optimizations of product commitments to assist the pricing and allocating of energy volumes.
Users interactions with the system correspond to the organizational layers (cf. Figure 2, right). User
roles range from the personal, which is responsible for the Service and Maintenance of individual units
(bottom) to Energy Traders (top), who query aggregated product commitments and set the aggregation
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
control parameters. Other example users are Plant Operators and the Platform Operation, who parameterize the interplay of Unit Agents and Pool Controllers.
The Unit and Accesss Layers, together with their associated user interfaces, provide the functionality of
SCADA systems, i.e. units can be monitored and controlled remotely. The overlying layers add levels of
control. While agents monitor the individual activities and ensure the commitments of individual units, the
Pool Control plans for the availability of individual Energy Products, e.g. control reserves (cf. Section 2).
The Product Layer abstracts the internals of the Pool Layer and aggregates product types for trading.
Figure 2
Layered architecture of the VPP control platform.
4.2
Integrating Adaptivity
By definition, VPPs operate in a volatile environment and thus require self-adaptive features to maintain
an operational state (see Section 2). In order to integrate self-adaptive features in a software application,
the state of (sub-) systems has to be controlled by a feedback loop. The required activities have been
summarized by Dobson et al (2006) as an Autonomic Control Loop (ACL, cf. Figure 3, I). Continuously,
information about the systems state are collected and analysed. Based on the current state and/or the expected future states of the system, adjustments are decided and enacted.
Within the research project “Selbstorganisation durch Dezentrale Koordination in Verteilten Systemen”
(SodekoVS), 10 a development framework has been devised for equipping software systems with decentralized, self-organizing coordination algorithms (Sudeikat 2010, Sudeikat/Renz 2010). In addition to a
tailored programming model, the architectural approach facilitates embedding ACL activities. An agent
10
Self-Organization by Decentralized Coordination in Distributed Systems
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
programming model has been enhanced with the ability to attach monitoring components to individual
agents. Dedicated agent modules observe the execution and decide, based on a model of the intended coordination behaviour, if the agent activities have to be adjusted. We propose to adopt this approach to
equip the VPP platform with fault correction strategies. Furthermore, self-managing aspects can be embedded as well.
The resulting monitoring framework is depicted in Figure 3 (II). The components within the VPP Platform form a network of service providers and consumers. These networks steer the individual VPPs and
form the Application Level. User interfaces (cf. Figure 2) convey views on the functional aspects of the
systems operation. Furthermore, the component model is extended with the ability to embed modules for
the component supervision. The monitoring of invariants and exception handling mechanisms are encapsulated in these modules. These additional elements create a Supervision Level. Each component is autonomously supervised. Gathering the analysis results from the individual supervisions allows to construct a
system-wide view on the state of system operation.
Figure 3
The Autonomic Control Loop (I, adapted from Dobson et al 2006) and the conceptual model for the integration of the systems supervision at run-time (II).
5.
Discussion and Related Work
The utilization of agent-oriented designs for power system control have been argued by several groups
(e.g. McArthur et al 2007, Ramchurn et al 2012). Due to the volatile execution context and the
significance of the managed infrastructures in the field, it is particularly attractive to model power system
elements as autonomous actors. While VPP control systems are typically structured as central-hierarchical
organizations (Rohbogner 2012), we value integrating self-aware designs and autonomous abilities via
agents, as it enables software components that collaborate proactively to resolve failure conditions.
Software agents have been argued as a conceptual extension to web service technologies (e.g. see Huhns
2002). Our approach aims at a coexistence of agents and conventional i.e. service-oriented components. In
addition, we realize the blueprint by gradually enhancing agent models. Simple, reactive realizations are
used to introduce agent autonomy and the sophistication of agents is successively raised. Conventional
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
web-service communication means support application interoperability, agent and pool controls have a
high communicative throughput. Thus the current development is based on lightweight communication
and data models.
The International Electrotechnical Commission (IEC) provides a reference architecture in order to
correlate the standardisation activities within the Technical Committee 57 and outline the usage of the
multitude of standards for system implementation (IEC 2003). While the described architectural approach
abstracts from specific technologies and applications, it can be positioned within this architecture. The
first three layers of this reference architecture define means for application integration via (1) messaging
middleware, (2) data representation and (3) application interfaces. In the subjacent layer (4) applications
are positioned, ranging among others from SCADA systems to external IT Apps (see IEC 2003, page 18).
The here presented layers map horizontally within this layer. The Access Layer (see Figure 2) refers to
SCADA applications which can be used for remote unit control. The Product Layer (see Figure 2) refers to
interfacing external IT applications, which IT system are used to managed the product commitments.
Reference architectures for controlling power systems are inherently interdisciplinary as they combine
the control of power system elements and the integration of flows of control decisions in/out enterprise IT
systems. A software-oriented, vendor-specific example is the Smart Energy Reference Architecture
(Microsoft Worldwide Power & Utilities Group, 2013). A vital design aspect is the application integration
(ibid, p. 96): service-based control & enterprise applications (process-centric application), data sources
(database-centric application), grid elements & field units (grid integration), and users via web-based
access (web integration). The identified main functionalities for VPP control (a-d, see Figure 1, II)
conform to these areas. We explicitly distinguish the communication infrastructure (e, see Figure 1) as a
means for integrating the applications types. In the layered system structure, Grid integration is handled by
the Access Layer and user integration is understood as a crosscutting aspect, due to the differing
information needs of end-users. Pool and Product Layers structure a VPP specific extract of system
elements. These are interconnected by a customized communication bus and supportive crosscutting
functionalities, e.g. the location transparency of data items and automated archiving facilities.
6.
Conclusions
In this paper, we discussed requirements for tomorrows VPP control systems. We argue that the software
support for controlling DERs should move from isolated, proprietary products to service- & agent-oriented
system architectures, which facilitate the composition of custom control systems. The rationale for this
customizability is the ability to adjust the control system design to changing market and regulatory conditions. We propose an abstract system architecture, which uses an agent-based control system design as a
mediator between the (re-)use of existing telecontrol systems and commitments for energy product trading.
The discussed architectural model is currently implemented within the national founded research project Smart Power Hamburg. Future work comprises the evaluation of control strategies and agent models
for different energy trading scenarios and VPP configurations. The agent-based overlay provides a testbed
for differing control strategies, for example the provision of control reserves (see Section 2). Moving from
initial, reactive agent designs, control strategies will be extended gradually. For growing numbers of modules and functionalities, the composition and deployment of VPP realizations is to be addressed, possibly
using domain specific language concepts.
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
Acknowledgements
The joint research project Smart Power Hamburg (www.smartpowerhamburg.de) is funded by the Federal
Ministry of Economics and Technology (BMWi)11 within the EnEff:Wärme12 research initiative Within
this project Hamburg Energy GmbH cooperates with the Hamburg University of Applied Sciences and the
RWTH Aachen University.
Bibliography
Albersmann, J., Bahn, D., Baum, I., Farin, S., Fecht, T., Reuter, R., Stiefelhagen, T. (2012): Virtuelle
Kraftwerke als wirkungsvolles Instrument für die Energiewende, PricewaterhouseCoopers AG
Wirtschaftsprüfungsgesellschaft.
Bitsch, R., Erge, T., Zacharias, P. (2001): Technische Anforderungen an dezentrale Versorgungsstrukturen
in Europa Forschungsverbund Sonnenenergie / Themen, pp. 15-21.
Clarke G., Reynders, D., Wright E. (2004): Practical Modern SCADA Protocols: DNP3, 60870.5 and
Related Systems, IDC Technologies.
Dobson, S., Denazis, S., Fernández, A., Gaïti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F.,
Schmidt, N., Zambonelli, F. (2006): A survey of autonomic communications, ACM Trans. Auton.
Adapt. Syst., 1, 2, pp. 223-259.
Huhns, M.N. (2002): Agents as Web Services, IEEE Internet Computing, 6, 4, pp. 93-95.
IEC (2003): IEC 62357-TR Ed.1: Power system control and associated communications - Reference
architecture for object models, services and protocols, Reference number IEC/TR62357:2003(E).
Kostyk, T., Herkert, J. (2012): Societal implications of the emerging smart grid, Commun. ACM, ACM,
2012, 55, pp. 34-36.
McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F., Funabashi, T. (2007):
Multi-Agent Systems for Power Engineering Applications Part I: Concepts, Approaches, and Technical Challenges, Power Systems, IEEE Transactions on, 2007, 22, pp. 1743-1752.
Microsoft Worldwide Power & Utilities Group (2013): Smart Energy Reference Architecture, Version 2.0,
www.microsoft.com/enterprise/industry/manufacturing-and-resources/power-and-utilities/.
Pedersen, A.B., Hauksson, E.B., Andersen, P.B., Poulsen, B., Træholt, C., Gantenbein, D. (2010): Facilitating a Generic Communication Interface to Distributed Energy Resources: Mapping IEC 61850 to
RESTful Services, Smart Grid Communications (SmartGridComm), pp. 61-66.
Ramchurn, S. D., Vytelingum, P., Rogers, A., Jennings, N. R. (2012): Putting the 'smarts' into the smart
grid: a grand challenge for artificial intelligence, Commun. ACM, 55, 4, pp. 86-97.
Robu, V., Kota, R., Chalkiadakis, G., Rogers, A., Jennings, N. R. (2012): Cooperative virtual power plant
formation using scoring rules, Proceedings of the 11th International Conference on Autonomous
Agents and Multiagent Systems - Volume 3, pp. 1165-1166.
Rohbogner, G., Fey, S., Hahnel, U., Benoit, P., Wille-Haussmann, B. (2012): What the term agent stands
for in the Smart Grid - definition of agents and multi-agent systems from an engineer's perspective.
Computer Science and Information Systems (FedCSIS), pp. 1301-1305.
Singh, M. P., Huhns M. N. (2005): Service-Oriented Computing: Semantics, Processes, Agents, John
Wiley & Sons Ltd.
Sterling, L., Taveter, K. (2009): The Art of Agent-Oriented Modeling, The MIT Press.
Sudeikat, J., (2010): Engineering Self-Organizing Dynamics in Distributed Systems: A Systemic
Approach, Südwestdeutscher Verlag für Hochschulschriften.
Sudeikat, J., Renz, W. (2010): On the Modeling, Refinement and Integration of Decentralized Agent
11
12
In German: Bundesministerium für Wirtschaft und Technologie
http://www.eneff-stadt.info/en/research-initiatives/the-eneffwaerme-research-initiative/
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5
Coordination – A Case Study on Dissemination Processes in Networks, 1st Int. Workshop
on Self-Organizing Architectures (SOAR 2009), 6090 LNCS, Springer Verlag, pp.251-274.
Sudeikat, J., Steghöfer J.-P., Seebach, H., Reif W., Renz W., Preisler, T., Salchow P. (2012): On the combination of top-down and bottom-up methodologies for the design of coordination mechanisms in
self-organising systems, Information and Software Technology, 54, 6, pp. 593-607.
Tröschel, M., Appelrath, H.-J. (2009): Towards Reactive Scheduling for Large-Scale Virtual Power
Plants, Multiagent System Technologies, MATES 2009, LNAI 5774, pp. 141-152.
UCTE (2004): UCTE Operation Handbook, Policy 1: Load-Frequency Control and Performance,
https://www.entsoe.eu/publications/system-operations-reports/operation-handbook/.
Uslar, M., Schmedes, T., Lucks, A., Luhmann, T., Winkels, L., Appelrath, H.-J. (2005): Interaction of
EMS related systems by using the CIM, Proceedings of the ITEE 2005, Shaker, 2005, pp. 596-610.
Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5