IEEE Transactions on Control Systems Technology · 2014 · 193 citations · 25 references
ManagementEconomicsEnergy ManagementEnergy SystemsPricing StrategyDemand ResponseElectricity Load ManagementEnergy Management SystemsEnergy PricingSmart Grid ManagementSmart GridsDistributed Energy SystemsElectricity PricingEnergy ConsumptionEnergy ControlLoad CurtailmentNoncooperative GamesOptimal Energy SchedulingSmart Grid Demand Response
Energy consumers adjust usage based on price to balance cost and load curtailment, and their decisions interact through total‑load‑based pricing. The paper proposes a pricing‑based distributed energy control strategy to eliminate peak load in smart grids and develops an algorithm with proven convergence. The strategy models consumer interactions as a noncooperative game, employs a distributed algorithm that starts every 15‑minute slot, and applies to HVAC systems, with a unique equilibrium and convergence guarantees. Simulations demonstrate the strategy removes peak load, balances supply and demand, and the algorithm converges to equilibrium.
This brief proposes a pricing-based energy control strategy to remove the peak load for smart grid. According to the price, energy consumers control their energy consumption to make a tradeoff between the electricity cost and the load curtailment cost. The consumers are interactive with each other because of pricing based on the total load. We formulate the interactions among the consumers into a noncooperative game and give a sufficient condition to ensure a unique equilibrium in the game. We develop a distributed energy control algorithm and provide a sufficient convergence condition of the algorithm. The energy control algorithm starts at the beginning of each time slot, e.g., 15 min. Finally, the energy control strategy is applied to control the energy consumption of the consumers with heating ventilation air conditioning systems. The numerical results show that the energy control strategy is effective in removing the peak load and matching supply with demand, and the energy control algorithm can converge to the equilibrium.
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