Optimal Net-Load Balancing in Smart Grids with High PV Penetration

Mitigating Supply-Demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as Demand Response (DR) have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for Smart Grids with high PV penetration. These grids can sometimes exhibit supply surplus causing over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are complex and computationally expensive. This increases the complexity of net-load balancing systems used by the grid operator and limits their scalability. Recently new technologies have been developed that enable the rapid and selective connection of PV modules of an installation to the grid. Taking advantage of these advancements, we develop a unified optimal net-load balancing framework which performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and develop bounded approximation algorithms for minimizing the curtailment cost. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation. We also incorporate the notion of fairness to ensure that curtailment is evenly distributed among all the nodes. Finally, we develop an online algorithm which performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions which are close to optimal in a small amount of time.Comment: 11 pages. To be published in the 4th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 17) Changes from previous version: Fixed a bug in Algorithm 1 which was causing some min cost solutions to be misse

Integrating supply and demand-side management in renewable-based energy systems

Demand-Response (DR) has emerged as a valuable resource option for balancing electricity supply and demand. However, traditional power system models have neglected to include DR within long-term expansion problems. We can summarize our scientific contributions in the following aspects: (i) design of a new integrated co-optimization planning model for supply and demand coordination; (ii) assessment of the technical and economic impact of DR for systems with a high share of Renewable Energy Sources (RES) and (iii) proposal of the ‘opportunity cost’ concept for computing the price of not meeting the demand. Findings of this research support the hypothesis that DR scenarios reveal a high potential for delaying future investments in power capacity compared to scenario BAU (Business as Usual). However, it was found a limited potential of DR to integrate additional renewable plants. This research has provided further evidence concerning the potential of DR to decrease the levels of CO2 emissions that is strictly related to the reduced need for fossil fuel thermal power plants. Given the high RES share, uncertainties related to future weather conditions must be however highlighted. This study concludes on the importance of DR for power systems planning and lays the groundwork for future research.This work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT e Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202