An Aggregated Model for Energy Management Considering Crowdsourcing Behaviors of Distributed Energy Resources

Abstract

Increasing deployment of distributed energy resources (DERs) is re-sculpturing the modern power systems in recent years. Future smart power distribution systems should be competent at accommodating extensive integration of DERs and managing the associated uncertainties at the distribution level. The electricity market has been proved to be an efficient way to employ market signals to direct behaviors of users and DERs with large capacity and homogeneous pattern. However, existing market frameworks cannot effectively handle a large number of small-scale DERs due to their diverse characteristics and arbitrary behavior patterns. In this context, an aggregated model which can represent and manage a diverse collection of DER, load, and storage is proposed. An additional trading platform, namely the energy sharing market, is established to reinforce the coordination and collaboration among various aggregators as well as operators. Energy sharing scheme is applied and a corresponding dynamic dispatch platform is designed to solve the crowdsource problem. The efficiency of the proposed model is validated by the numerical studies, and the market performance and impacts of energy sharing on the power systems are illustrated.INDEX TERMS Distributed power generation, electricity supply industry deregulation, energy management, energy sharing, crowdsourcing behavior.

Impact of renewable energy in Indian electric power system

“…Fan et al [37] have analyzed the impact of RES into power system through demand response approach and presented the impact of demand response/comfort loss tolerance on integrated RES. Liang et al [38] have suggested an aggregated model for distributed energy management through crowd sourcing behaviors and validated the same by numerical studies by analyzing market performance and energy sharing.…”

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

“…In Reference [67], a state feedback controller was proposed for the stabilization of an uncertain linear networked control systems with random communication time delays, which differs from the Lyapunov-Razumikh methodology. In Reference [68], a sufficient condition was obtained for the exponential stability of the networked systems, and they also the mentioned relationship between the dropout rate of the data-packets. An occurrence-based discrete-time model (an exponential unsure system with delay) was proposed by Reference [69] that showed that the stability of the projected system can be achieved by finding an effect for a switched stochastic system with an additive norm delimited uncertainty.…”

“…It is to be noted that the constraint on delays due to the network is not simple for some NCSs. So, to get rid of this constraint, efforts are created; see, e.g., References [49,68,88].…”

“…Type of Delay Delay/Delay Range (in ms) [41] Random Delay 160 to 132 [45] Network-Induced Time Delay 15 to 35 [49] Random Delay 0 to 2000 [50] Time-Varying Delay 0 to 1000 [52] Random Delay 0 to 1000 [56] Random Delay 0 to 3000 [66] Random Delay 2000 to 6000 [67] Random Delay 0 to 123 [68] Network Induced Delay 0 to 100 [70] Network Induced Delay 500 to 1550 [80] Variable Delay 104 to 169…”

New Market Model with Social and Commercial Tiers for Improved Prosumer Trading in Microgrids

“…The load flexibility can change the trading offers. Other perspectives consider peak loads in the prosumer's vicinity with smart P2P subscribed capacity prices in [67], or the crowdsourcing concept for surplus energy planning or sharing, as is used in [68], or considering the indispensable local energy storage systems [69].…”