Synthesised Constraint Models for Distributed Energy Management

Alexander Schiendorfer, Jan-Philipp Steghöfer, Wolfgang Reif
2014 Proceedings of the 2014 Federated Conference on Computer Science and Information Systems  
Resource allocation is a task frequently encountered in energy management systems such as the coordination of power generators in a virtual power plant (unit commitment). Standard solutions require fixed parametrised optimisation models that the participants have to stick to without leaving room for tailored behaviour or individual preferences. We present a modelling methodology that allows organisations to specify optimisation goals independently of concrete participants and participants to
more » ... ft more detailed models and state individual preferences. While considerable efforts have been spent on devising efficient control algorithms and detailed physical models in power management systems, practical aspects of unifying several heterogeneous models for optimisation have been widely ignored -a gap we aim to close. As a by-product, we give a formulation of warm and cold start-up times for power plants that improves existing power plant models. The concepts are detailed with the loaddistribution problem faced in virtual power plants and evaluated on several random instances where we observe that a significant number of soft constraints of individual actors can be satisfied if considered. I. CONSTRAINT OPTIMISATION PROBLEMS IN POWER SYSTEMS R ESOURCE allocation and scheduling are difficult problems that occur frequently in energy systems, be it the coordination of power generation [1], demand-side management, or building control software. In a producer-based view, supply needs to meet the demand as accurately as possible in order to guarantee stability and avoid costs incurred by corrective measures. Similarly, consumers may try to find cost-minimising schedules for processes required throughout a day with respect to time-dependent energy prices. Current initiatives 1 are based on the assumption that groups of prosumers (i.e., energy producers and/or consumers) can form and team up to achieve better prices or production rates for their participants. We also adopt the notion of agents, indicating that the prosumers are in principle autonomous entities, even if they surrender the decision about their power output to the group. A straightforward solution (see, e.g., [2], [3], [4], [5] ) to this resource allocation problem is to model the decision making process (e.g., distributing the load in a virtual power plant (VPP) or scheduling energy-consuming domestic processes in a consumer coalition) as a mathematical optimisation problem such as a mixed integer program (MIP), a linear program 1 cf. https://www.energiekosten-stop.at/ for consumer alliances or http: //www.swm.de/geschaeftskunden/effizienz-umwelt/virtuelles-kraftwerk.html for virtual power plants (LP) or as a constraint satisfaction and optimisation problem (CSOP) as done by industrial distributed energy management tools such as Siemens DEMS [6] or PLEXOS Integrated Energy Model [7] . DEMS is used, e.g., by the municipal utility of the city of Munich for controlling a VPP [8] . In essence, the problem is specified in terms of (decision) variables, their associated domains, and constraints that regulate which assignments are valid. The task accomplished by the respective solvers is then to assign values to all variables such that no constraint is violated and an optimisation objective is minimised (or maximised). Typically, such tools (DEMS in particular) offer a predefined range of agent types such as energy generators, storages, or controllable loads. Users may then specify the topology of their energy system to calculate optimized power schedules. A concrete power generator is thus essentially represented by one tuple in a data repository containing the parameters defining its behaviour. Consequently, the provided models constitute a static one-for-all solution that needs to encompass all supported characteristics of power generators, including, e.g., time-dependent properties such as inertia. Clearly, power generators show varying characteristics such as change rates, cool or warm start-up times or power boundaries depending on, e.g., the power plant type or manufacturer. Parametrised models as described above cannot support this variety. At some point the model has to be fixed for all participants and individual variables necessary to model a certain constraint cannot be added. To overcome this limitation, we suggest to synthesise an optimisation problem from several individual models. Such synthesised models allow for individual preferences (typically in the form of knowledge acquired by power plant operators such as economically optimal production ranges or limited ramp-up or -down of a generator) and separate modelling of the organisational optimisation problem and physical models of individual participants -properties that are attractive for organisations as more clients can be served as well as for individual participants as they can influence the assigned plans. This methodology is not only nice to have in multi-agent systems, where optimisation problems result from a combination of several sub-problems -it is necessary. Our contribution leads to a methodology that offers: 1) support for heterogeneous prosumers requiring specific sets of variables; 2) isolated modelling of physical components; 3) clean separation of the organisational aspects such as
doi:10.15439/2014f49 dblp:conf/fedcsis/SchiendorferSR14 fatcat:35g6xihwbjdufbiim6l4sv242y