The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of ...The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology （Self-Organizing Maps and Statistical Ward＇s Linkage） to classify high electricity market prices is analysed. Besides, with the help of Non-Parametric Estimation, some price-patterns were found in the abovementioned clusters. The contained knowledge within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.展开更多
The increasing penetration of Distributed Generation(DG)and Demand Responsive(DR)loads in power systems has necessitated the development of novel approaches to address the coordination problem of Price Responsive Devi...The increasing penetration of Distributed Generation(DG)and Demand Responsive(DR)loads in power systems has necessitated the development of novel approaches to address the coordination problem of Price Responsive Devices(PRD).These PRDs are treated as self-interested players aiming to optimize their consumption patterns based on prevailing prices.In this paper,we propose a new algorithm based on the Dantzig-Wolfe(DW)Decomposition method,which tackles the coordination problem of self-interested PRDs in a distributed manner.Leveraging the distributed nature of the DW approach,we model the self-interested algorithms of PRDs as sub-problems within the DW framework.The coordinator,or grid operator,responsible for collecting the energy consumption information(energy bids)of PRDs,solves the master problem of the DW and determines the price signal accordingly.The proposed algorithm exhibits fast convergence as the sub-problems within DW,which could involve a large number of PRDs(potentially millions),can be solved simultaneously.Additionally,based on the DW theory,if the PRDs’subproblems are convex,reaching the optimal point(equivalent to Nash Equilibrium)is guaranteed within a limited number of iterations.To evaluate the proposed model,we conducted a simulation involving 200 participant households,each equipped with two types of loads:Electric Vehicles(EVs)as examples of inter-ruptible loads,and Electric Water Heaters(EWHs)as examples of Thermostatically Controlled Loads(TCLs).The results demonstrate that when the algorithm converges to the optimal point,both the generation cost and user payment(based on the marginal cost of generation)decrease.Furthermore,there is a significant reduction in the Peak to Average Ratio(PAR)of the aggregate load.展开更多
文摘The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology （Self-Organizing Maps and Statistical Ward＇s Linkage） to classify high electricity market prices is analysed. Besides, with the help of Non-Parametric Estimation, some price-patterns were found in the abovementioned clusters. The contained knowledge within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.
文摘The increasing penetration of Distributed Generation(DG)and Demand Responsive(DR)loads in power systems has necessitated the development of novel approaches to address the coordination problem of Price Responsive Devices(PRD).These PRDs are treated as self-interested players aiming to optimize their consumption patterns based on prevailing prices.In this paper,we propose a new algorithm based on the Dantzig-Wolfe(DW)Decomposition method,which tackles the coordination problem of self-interested PRDs in a distributed manner.Leveraging the distributed nature of the DW approach,we model the self-interested algorithms of PRDs as sub-problems within the DW framework.The coordinator,or grid operator,responsible for collecting the energy consumption information(energy bids)of PRDs,solves the master problem of the DW and determines the price signal accordingly.The proposed algorithm exhibits fast convergence as the sub-problems within DW,which could involve a large number of PRDs(potentially millions),can be solved simultaneously.Additionally,based on the DW theory,if the PRDs’subproblems are convex,reaching the optimal point(equivalent to Nash Equilibrium)is guaranteed within a limited number of iterations.To evaluate the proposed model,we conducted a simulation involving 200 participant households,each equipped with two types of loads:Electric Vehicles(EVs)as examples of inter-ruptible loads,and Electric Water Heaters(EWHs)as examples of Thermostatically Controlled Loads(TCLs).The results demonstrate that when the algorithm converges to the optimal point,both the generation cost and user payment(based on the marginal cost of generation)decrease.Furthermore,there is a significant reduction in the Peak to Average Ratio(PAR)of the aggregate load.
