On Using Semantic Complex Event Processing for Dynamic Demand Response Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna Department of Computer Science, FZ University of Southern California, Los Angeles Submitted to SmartGridComm 2012

Semantic Information Modeling for Emerging Applications in Smart Grid Demand Response Objectives Manage demand-side power load in response to supply conditions Reduce the required maximum power generation capacities Avoid starting and stopping generation units frequently The state of the art Schedule-based Incentive-based (1) schedule-based, where curtailment is scheduled a priori based on customer commitments, and (2) incentive-based, where customers are offered pricing incentives during peak load periods to encourage voluntary curtailment [2]. When there is limited co mmunication and monitoring info. This is what we can do 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Smart Power Grid Fine-grained monitoring of power grid activities Spatial scale – spaces, devices, users and organizations Temporal scale – interval based measurements Indirect influencers – weather forecasts, traffic conditions, event schedules Bi-directional communications Management sites to end-use customers/devices End-use customers/devices to management sites Power grid is under rapid transition to smart grid. Featured by Upgrading generation/transmission/distribution Integrating digital and information technologies Leading to more efficient and reliable management of power systems 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Smart Grid Architecture Advanced IT applications Bi-directional communication networks Information sources sensors, meters, smart appliances, online services … 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Dynamic DR in Smart Grid A New DR Paradigm Leverage fine-grained Smart Grid monitoring information Coordinate relevant information from multiple domains Perform online information analysis for decision making Solution Framework - Semantic Complex Event Processing Abstract information as events associated with domain entities Model decision making situations as event patterns Process event streams to detect occurrences of patterns in real-time Model domain-specific situations as information/event patterns Perform online analysis of information streams for timely situation detection Real-time monitoring information – event data tuples Domain entities and knowledge – semantic meta data DR situations – combinations/patterns of semantic events 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Contributions DR Pattern Taxonomy Categorize DR event patterns from different aspects Offer example patterns in a campus micro grid scenario Experimental Evaluation Demonstrate the use of the SCEP framework for DR Perform online analysis of campus micro grid data streams to detect DR situations Exploit capabilitie sof scep for DR applications 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Campus Micro Grid Grid Infrastructures >170 buildings Thousands of class rooms, meeting rooms lecture halls … Numerous meters, sensors to measure from campus to device level consumption, temperature, airflow and occupancy changes … DR Participants Over 6,000 students, faculty and staff Grouped in various organizations – schools, departments, facility management services Evolving, multi-user role 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Complex Event Processing Semantic Events Real-time data streams linked with domain ontologies DR Event Patterns High level queries defined over ontologies and streams 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid DR Pattern Taxonomy Top Level Categories Classify DR patterns from different perspectives Exploit the capabilities of SCEP in modeling DR applications To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid End-use Purpose Monitoring an Prediction Patterns – when to perform DR (incrementally) Curtailment Patterns – how to perform DR To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Monitoring Pattern Demand Monitoring Response Monitoring To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Prediction Pattern Space specific short-term predictions Traditional demand forecast models are not well suited for demand prediction at fine temporal and spatial scale, particularly when consumption profiles change [5]. However can be captured by intuitive patterns To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Curtailment Pattern Opportunistic versus Scheduled and Voluntary Shave Pattern Eliminate non-necessary or wasteful demands Shift Pattern Move non-urgent demand Shape Pattern Flatten demand To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Spatial and Temporal Scale Define patterns based on objective spaces Detect patterns at different time scales of data streams To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Spatial Scale Physic Space and Equipment Virtual Space customer groups, departments and so on To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Representation, Life Cycle and Adaptivity Pattern abstraction level Life Cycle Time periods during which a pattern is active Adaptivity Pattern evolving over time? To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Case Study in Campus Micro Grid (1) Event Streams Event Patterns Example pattern 1-6 To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Case Study in Campus Micro Grid (2) Empirical Evaluations To fully exploit the capabilities of semantic complex event processing for DR applications, we classify DR patterns from different perspectives. Figure 2 shows top level categories of this taxonomy, discussed further below. The examples we provide are based on semantic concepts for and experiences in the USC microgrid, but can be generalized to other environments. 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Discussion The state-of-the-art DR approaches are based on static planning Smart Grid provides opportunities for more fine-grained DR in both spatial and temporal scale Semantic complex event processing is a promising information processing paradigm for dynamic DR The CEP based data-driven opportunistic DR is a vital supplement to traditional schedule an incentive driven approaches The next step is to extend the case studies on campus 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid

Semantic Information Modeling for Emerging Applications in Smart Grid Thank You! 18-Sep-18 Semantic Information Modeling for Emerging Applications in Smart Grid