Scenario-based Economic Dispatch with Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers

Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System

A Data-Driven Policy for Addressing Deployability Issue of FMM FRPs: Resources Qualification and Deliverability

Intensified netload uncertainty and variability led to the concept of a new market product, flexible ramping product (FRP). The main goal of FRP is to enhance the generation dispatch flexibility inside real-time (RT) markets to mitigate energy imbalances due to ramp capability shortage. Generally, the FRP requirements are based on system-wide or proxy requirements, so the effect of FRP awards on the transmission line constraints is not considered. This can lead to FRP deployability issues in RT operation. This paper proposes a new FRP design based on a datadriven policy incorporating ramping response factor sets to address FRP deployability issue. First, a data-mining algorithm is performed to predict the ramp-qualified generators to create the data-driven policy. Then, the FRP awards are assigned to these units while considering effects of post-deployment of FRPs on the transmission line limits. Finally, the proposed data-driven policy is tested against proxy policy through an out-of-sample validation phase that (i) mimics the RT operation of the CAISO, and (ii) represents the expensive ad-hoc actions needed for procuring additional ramping capability to follow realized netload changes. The results show the effectiveness of the proposed data-driven policy from reliability and economic points of vie

Assessment of the operational flexibility of virtual power plants to facilitate the integration of distributed energy resources and decision-making under uncertainty

Distributed energy resources (DERs) are elements that actively participate in the supply of renewable energy and contribute to the decarbonization of the power system. However, they lack two factors necessary to take advantage of their operational flexibility: observability and controllability. In this sense, Virtual Power Plants (VPPs) are a feasible alternative to provide the necessary requirements for the optimal management of a set of distributed units. Therefore, knowledge of the technical and energy characteristics of each unit that makes up the VPP is a necessary condition for the effective integration of DERs into the power system. This paper proposes a methodology to graphically represent, quantify and exploit the aggregate operational flexibility of a set of units. The proposed methodology is based on five metrics related to active and reactive power, which serve as a tool to facilitate the VPP Operator's decision-making under uncertainty. Consequently, achieving the coordinated operation of several distributed units makes it possible to achieve common objectives. For instance, frequency and voltage regulation, compliance with a planned power curve, or dealing with the variability of renewable energies. The proposal is applied to a theoretical case study and through real operational tests between a hydroelectric unit and a photovoltaic plant. Finally, it is shown that the results obtained are a useful tool in real-time.The authors acknowledge the support from GISEL research group IT1191-19, as well as from the University of the Basque Country UPV/EHU (research group funding 181/18)