Recently there has been a considerable increase in the penetration level of distributed energy resources (DERs) due to various factors, such as the increasing affordability of these resources, the global movement towards sustainable energy, and the energy democracy movement. However, the uncertainty and variability of DERs introduce new challenges for power system operations. Advanced techniques that account for the characteristics of DERs, i.e., their intermittency and human-in-the-loop factors, are essential to improving distribution system operations. This paper proposes a behavior-aware approach to analyze and aggregate prosumers’ participation in demand response (DR) programs. A convexified AC optimal power flow (ACOPF) via a second-order cone programming (SOCP) technique is used for system scheduling with DERs. A chance-constrained framework for the system operation is constructed as an iterative two-stage algorithm that can integrate loads, DERs’ uncertainty, and SOCP-based ACOPF into one framework to manage the violation probability of the distribution system’s security limits. The benefits of the analyzed prosumers’ behaviors are shown in this paper by comparing the optimal system scheduling with socially aware and non-socially aware approaches. The case study illustrates that the socially aware approach within the chance-constrained framework can utilize up to 43% more PV generation and improve the reliability and operation of distribution systems.
The penetration of distributed energy resources (DERs) is increasing dramatically. Due to the uncertainty of DERs, the operation of the distribution system is facing higher risks and challenges. To overcome such challenges, a two-stage chance-constrained convex AC optimal power flow (ACOPF) model is proposed in this paper, which can increase the economic efficiency of distribution system operation and manage the intermittency of DERs. In the first stage, a convex second-order cone programming (SOCP)-based ACOPF model is proposed in which the detailed models and limitations of DER, namely, demand response (DR), energy storage units, and rooftop PV systems are modeled to obtain participation ratio of DERs. In the second stage, Monte Carlo simulation is utilized to model the uncertainties of DERs. A probability violation index is introduced to make a trade-off between scheduling more DERs and imposing a higher risk to the distribution system. In this stage, power flow analysis is conducted for each scenario to determine the probability violation index of system. Then, a modified SOCPbased ACOPF is proposed to satisfy the system probability violation criterion. Simulation results illustrate that the proposed two-stage chance-constrained model improves economic efficiency and reliability of real-time operation of the distribution system.
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