Finite local consistency characterizes generalized scoring rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Expert Systems with Applications: An International Journal
The complexity of safe manipulation under scoring rules
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
A scoring rule-based mechanism for aggregate demand prediction in the smart grid
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Eliciting forecasts from self-interested experts: scoring rules for decision makers
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Intelligent power management in micro grids with EV penetration
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA-EP-GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers' payment.