Chapter
Chapter 2: Active Consumer Characterization and Aggregation
2.2. Overview of the interaction between aggregator and other system players
2.2.3. Deregulated players
2.3. Consumption modeling and flexibility forecasting
2.3.1. Consumer segmentation
2.3.2. Forecasting baseline demand
2.3.3. Forecasting flexibility under a dynamic pricing scheme
2.3.3.1. Disaggregation and generation of consumer samples
2.3.3.2. End-user model simulation
2.3.3.3. Aggregation and averaging of the response curves
2.3.4. Calibration of price sensitivity parameters
2.4. Algorithms for electricity market price forecasting
2.4.1. Short-term energy price forecasting
2.4.2. Short-term energy price volatility forecasting
2.5. Optimization algorithm for designing demand response-based offers for the market
2.5.1. Aggregator toolbox optimization model for the day-ahead market
2.6. Software architecture of the aggregator toolbox
2.7. Numerical results on simulation experiments
2.7.1. Flexibility forecasting
2.7.2. Generating market offers
3: Distributed Intelligence at the Consumer’s Premises
3.2. Functional architecture
3.3. Software architecture
3.3.3. Software architecture layers
3.4. Classification of distributed energy resources
3.4.1. Non-controllable loads
3.4.5. Non-dispatchable generation sources
3.4.6. Dispatchable generation sources
3.5. Optimization algorithm for appliance scheduling
3.5.1. The optimization problem solved by the energy box
3.5.2. A mathematical model for energy box scheduling problems
3.5.3. A heuristic algorithm for energy box scheduling problems
3.6. Results on testing the implementation of the software architecture
4: Distribution Control Center: New Requirements and Functionalities
4.2. Functional specifications, including strategies
4.2.1. Distribution system operator’s algorithms and prototypes to enable and exploit demand response
4.2.1.1. Algorithms to enable demand response
4.2.1.2. Algorithms to exploit demand response
4.3. Architectures of distribution system automation and control
4.3.1. Centralized approach
4.3.2. Decentralized approach
4.3.2.1. Centralized versus decentralized approach
4.4. Active and reactive power control in medium-voltage active distribution grids
4.5. Validation of demand response products
4.5.1. Ex ante validation
4.5.2. Real-time validation
4.6. New operational planning applications for the medium-voltage control center
4.6.1.1. Load forecasting
4.6.1.2. Distributed generation forecasting
Chapter 5: Distribution Network Representation in the Presence of Demand Response
5.2. Requirements for distribution network monitoring and control
5.2.1. The functionalities at the distribution system operator control center level
5.2.2. Functionalities at the high-voltage/medium-voltage substation level
5.2.3. Functionalities at the medium voltage/low voltage level
5.3.1.1. Selection of key constraints
5.3.1.2. Impact of nodal injections on the grid constraints
5.3.1.2.1. Loading constraints
5.3.1.2.2. Voltage constraints
5.3.1.3. Clustering nodes
5.3.2.2. Nodal injections
5.3.2.3. Representation of the load area network
5.4. Load areas: study cases
5.5. Appendix: active–reactive relationships
5.5.2. Distributed generation
Chapter 6: Communication Needs and Solutions for the Deployment of Demand Response
6.2.1. System requirements
6.2.1.1. Interoperability requirements
6.2.1.2. Interoperability between vendors
6.2.1.3. Interoperability between actors
6.2.1.4. Physical media requirements
6.2.1.5. Scalability requirements
6.2.1.6. Regulatory requirements
6.2.1.7. Standardization requirements
6.2.2. Technical requirements
6.2.2.2. Plug-and-play requirements
6.2.2.3. Quality of service requirements
6.2.2.4. Firmware upgrading requirements
6.2.2.5. Security requirements
6.2.3. Economic requirements
6.3. Network architecture and communication technologies
6.3.1.1. Communication layer
6.3.1.3. Communication entities
6.3.2. Network layer requirements
6.3.3. Communication technologies
6.3.4. Technologies summary and conclusions
6.4. A communications solution for demand response
6.4.1. Software architecture
6.4.2. Anatomy of an interface
6.4.3. Concrete example from the ADDRESS project
6.4.3.1. Use case and its sequence diagram
6.4.3.2. Common information model extensions for ADDRESS
6.4.3.3. Message payload type or profile definition
6.4.4. Development and testing environment
6.4.4.1. Test setup 1: testing domain interfaces
6.4.4.2. Test setup 2: testing messaging and domain interfaces
6.4.4.3. Test setup 3: testing messaging and transport interfaces
6.4.4.4. Test setup 4: full deployment in local staging environment
6.5. Summary on communications for demand response
Chapter 7: System-level Benefits of Demand Response
7.2.1.1. Deferral of investments in new generation capacity
7.2.1.2. Reduced energy costs
7.2.1.3. Reduced price variability
7.2.1.4. Lower curtailment of variable renewable generation
7.2.1.5. More security of supply
7.2.2.1. Reduced network losses
7.2.2.2. Reduced network investments
7.2.2.3. Improved quality of service
7.3. Review of system benefits
7.3.1. Consumer flexibility and capacity to respond to active demand signals
7.3.1.1. Example 1: consumer flexibility scenarios in the ADDRESS project
7.3.1.2. Example 2: consumer flexibilities identified in the ADVANCED project
7.3.2.1. Results of benefits of active demand in generation systems from the ADDRESS project
7.3.3.1. Results from the ADDRESS project
7.3.3.2. Results from the ADVANCED project
Chapter 8: Techno-economic Analysis of Demand Response
8.2. Techno-economic analysis: identification of potential business cases for demand response in a networked business
8.2.1. Technical dimension
8.2.1.1. Technical capabilities of demand response flexibility
8.2.1.2. The grid: a physical reality
8.2.2. Economic dimension
8.2.3. Business cases for demand response in a networked business: market participants
8.2.3.3. Electricity producer
8.2.3.4. Balancing responsible party
8.2.3.7. Transmission system operator
8.2.3.8. Distribution system operator
8.2.3.9. Providers of ancillary services
8.2.3.10. ICT service providers
8.2.3.11. Other market players
8.2.4. ADDRESS business cases for demand response: interactions between market participants
8.3. Techno-economic analysis of demand response: examples
8.3.1. Categorization of possible demand response business cases
8.3.2. Energy-oriented demand response business case studies
8.3.2.1. Selection of business cases
8.3.2.2. Graphical modeling of the selected business cases
8.3.2.3. General data and information requirements definition and collection
8.3.2.4. Case-specific assumptions, business logic and detailed economic assessment
8.3.2.4.1. Management of energy imbalances for balancing responsible parties
8.3.2.4.2. Tertiary reserves for transmission system operators
8.3.2.4.3. Short-term load shaping to optimize purchases and sales for retailers
8.3.2.4.4. ADDRESS results of energy-oriented demand response services
8.3.3. Capacity-oriented demand response business case studies
8.3.3.1. Selection of business cases
8.3.3.2. Definition of test cases for the provision of capacity
8.3.3.2.1. Consumption profiles
8.3.3.2.2. Demand flexibility
8.3.3.2.3. Energy payback
8.3.3.2.4. Imbalance costs
8.3.3.3. Economic assessment
8.3.3.3.1. Provision of transmission network capacity
8.3.3.3.2. Distribution network imports management
8.3.3.3.3. Provision of distribution network capacity
8.3.3.4. Analysis of the results
9: Socioeconomic Aspects of Demand Response
9.2. Social aspects of demand response
9.3. Key elements of the ADDRESS project from the perspectives of participants
9.3.1. Summary of the trial
9.3.2. Theoretical perspective
9.3.3. Everyday practices in the ADDRESS trial
9.3.4. Motivations for taking part in the trial
9.4. The everyday of demand response
9.4.1. Technology: the Energy Box
9.4.1.2. Programming and scheduling
9.5.1. Thermal comfort: space heating
9.5.2. Thermal comfort: water heating
9.6. The future of demand response
Chapter 10: Looking Forward: Gaps and Enablers for Wide Scale Demand Response Deployment
10.2. Aggregation function
10.2.1.1. Local flexibility potential
10.2.1.2. Portfolio management
10.2.1.5. Rights and duties
10.3.1. Rules and markets
10.3.3.1. Usability of the technology
10.3.3.2. Contextual issues
10.4.1. Rules and markets
10.4.1.2. Coordination between system operators and validation of demand response programs
10.4.1.3. Control of the energy delivered/consumed in the balancing responsible party’s perimeter
10.4.1.4. Functional requirements of a regulated player buying flexibility services
10.5. Other deregulated players
10.5.1. Rules and markets
10.6.1. Rules and markets
10.7.1. Communications between market players
10.7.2. Communications for distribution system operators
10.7.3. Communications within the house
10.8. Future research and development
Appendix: From Requirements to Domain Interface Definition in Five Steps
A.1. (Optional) Step 1: use case as text and table
A.2. Step 2: use case as UML sequence diagram
A.3. Step 3: information model building (or: find business objects in canonical CIM or extend CIM)
A.4. Step 4: define profiles (message payload types) from CIM
A.5. Step 5: generate XSD, WSDL
Integration of Demand Response into the Electricity Chain
：Challenges, Opportunities and Smart Grid Solutions
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
