Real-time charging strategies, in the context of vehicle to grid technology, are needed to enable the use of electric vehicle fleets batteries to provide ancillary services. In this paper, we develop tools to manage charging and discharging in a fleet to track an automatic generation control signal when aggregated. We propose a real-time controller that considers bidirectional charging efficiency and extend it to study the effect of looking ahead when implementing model predictive control. Simulations show that the controller improves tracking error as compared with benchmark scheduling algorithms, as well as regulation capacity and battery cycling.

Demand response (DR) is a versatile way of providing flexibility in power systems. In order to manage the flexibility of a large number of scattered DR resources, in the context of electricity markets, they must be aggregated by a new participant called the DR aggregator. This paper presents an optimization model to determine the optimal operation of a DR aggregator that manages a portfolio of DR programs in wholesale electricity markets. The aggregator is considered to be a strategic participant in the real-time market. The portfolio is composed of various contracts of load curtailment and flexible loads that can be executed for hourly load change. Uncertainty of market prices and balancing requirements are represented through a set of scenarios based on historical data. The proposed model is a stochastic bi-level mathematical program that is reformulated as a mixed-integer linear program. Several case studies with numerical results are presented.

There is an increasing necessity for new long-term planning models to adequately assess the flexibility requirements of significant levels of short-term operational uncertainty in power systems with large shares of variable renewable energy. In this context, this paper proposes an adaptive robust optimization model for the generation and transmission expansion planning problem. The proposed model has a two-stage structure that separates investment and operational decisions, over a given planning horizon. The key attribute of this model is the representation of daily operational uncertainty through the concept of representative days and the design of uncertainty sets that determine load and renewable power over such days. This setup allows an effective representation of the flexibility requirements of a system with large shares of variable renewable energy, and the consideration of a broad range of operational conditions. To efficiently solve the problem, the column and constraint generation method is employed. Extensive computational experiments on a 20-bus and a 149-bus representation of the Chilean power system over a 20-year horizon show the computational efficiency of the proposed approach, and the advantages as compared to a deterministic model with representative days, due to an effective spatial placement of both variable resources and flexible resources.

This paper presents the mathematical formulation and architecture of a robust energy management system for isolated microgrids featuring renewable energy, energy storage, and interruptible loads. The proposed strategy addresses the challenges of renewable energy variability and forecast uncertainty using a two-stage decision process combined with a receding horizon approach. The first-stage decision variables are determined using a cutting-plane algorithm to solve a robust unit commitment; the second stage solves the final dispatch commands using a three-phase optimal power flow. This novel approach is tested on a modified International Council on Large Electric Systems (CIGRE) test system under different conditions. The proposed algorithm is able to produce reliable dispatch commands without considering probabilistic information from the forecasting system. These results are compared with deterministic and stochastic formulations. The benefits of the proposed control are demonstrated by a reduction in load interruption events and by increasing available reserves without an increase in overall costs.

A distributed control system is proposed which uses the Conservative Power Theory (CPT) and a consensus algorithm to share imbalance and harmonics between different converters in three-phase four-wire droop-controlled Microgrids (MGs). The CPT is used to identify the balanced, unbalanced and distorted components of the currents and powers in the system. Control loops based on virtual impedance and implemented in the stationary a-b-c frame are then used to distribute these components between the various converters in the MG. The magnitudes of the virtual impedances are adaptively calculated using a novel consensus-based distributed control scheme with the aim of sharing imbalances and harmonics according to the residual VA capacity of each converter whilst regulating the imbalance and distortion at their output to meet the appropriate IEEE power quality standards. Extensive simulations show that the proposed distributed control has excellent performance, and experimental validation is provided using a laboratory-scale 4-wire MG.