Dispatching Stochastic Heterogeneous Resources Accounting for Grid and Battery Losses

We compute an optimal day-ahead dispatch plan for distribution networks with stochastic resources and batteries, while accounting for grid and battery losses. We formulate and solve a scenario-based AC Optimal Power Flow (OPF), which is by construction non-convex. We explain why the existing relaxation methods do not apply and we propose a novel iterative scheme, Corrected DistFlow (CoDistFlow), to solve the scenario-based AC OPF problem in radial networks. It uses a modified branch flow model for radial networks with angle relaxation that accounts for line shunt capacitances. At each step, it solves a convex problem based on a modified DistFlow OPF with correction terms for line losses and node voltages. Then, it updates the correction terms using the results of a full load flow. We prove that under a mild condition, a fixed point of CoDistFlow provides an exact solution to the full AC power flow equations. We propose treating battery losses similarly to grid losses by using a single-port electrical equivalent instead of battery efficiencies. We evaluate the performance of the proposed scheme in a simple and real electrical networks. We conclude that grid and battery losses affect the feasibility of the day-ahead dispatch plan and show how CoDistFlow can handle them correctly

Reliable and Robust Cyber-Physical Systems for Real-Time Control of Electric Grids

Real-time control of electric grids is a novel approach to handling the increasing penetration of distributed and volatile energy generation brought about by renewables. Such control occurs in cyber-physical systems (CPSs), in which software agents maintain safe and optimal grid operation by exchanging messages over a communication network. We focus on CPSs with a centralized controller that receives measurements from the various resources in the grid, performs real-time computations, and issues setpoints. Long-term deployment of such CPSs makes them susceptible to software agent faults, such as crashes and delays of controllers and unresponsiveness of resources, and to communication network faults, such as packet losses, delays, and reordering. CPS controllers must provide correct control in the presence of external non-idealities, i.e., be robust, and in the presence of controller faults, i.e., be reliable. In this thesis, we design, test, and deploy solutions that achieve these goals for real-time CPSs. We begin by abstracting a CPS for electric grids into four layers: the control layer, the network layer, the sensing and actuation layer, and the physical layer. Then, we provide a model for the components in each layer, and for the interactions among them. This enables us to formally define the properties required for reliable and robust CPSs. We propose two mechanisms, Robuster and intentionality clocks, for making a single controller robust to unresponsive resources and non-ideal network conditions. These mechanisms enable the controller to compute and issue setpoints even when some measurements are missing, rather than to have to wait for measurements from all resources. We show that our proposed mechanisms guarantee grid safety and outperform state-of-the-art alternatives. Then, we propose Axo: a framework for crash- and delay-fault tolerance via active replication of the controller. Axo ensures that faults in the controller replicas are masked from the resources, and it provides a mechanism for detecting and recovering faulty replicas. We prove the reliable validity and availability guarantees of Axo and derive the bounds on its detection and recovery time. We showcase the benefits of Axo via a stability analysis of an inverted pendulum system. Solutions based on active replication must guarantee that the replicas issue consistent setpoints. Traditional consensus-based schemes for achieving this are not suitable for real-time CPSs, as they incur high latency and low availability. We propose Quarts, an agreement mechanism that guarantees consistency and a low bounded latency- overhead. We show, via extensive simulations, that Quarts provides an availability at least an order of magnitude higher than state-of-the-art solutions. In order to test the effect of our proposed solutions on electric grids, we developed T-RECS, a virtual commissioning tool for software-based control of electric grids. T-RECS enables us to test the proper functioning of the software agents both in ideal and faulty conditions. This provides insight into the effect of faults on the grid and helps us to evaluate the impact of our reliability solutions. We show how our proposed solutions fit together, and that they can be used to design a reliable and robust CPS for real-time control of electric grids. To this end, we study a CPS with COMMELEC, a real-time control framework for electric grids via explicit power setpoints. We analyze the reliability issues..

Modeling and optimization of the smart grid ecosystem

This monograph serves as a reference for researchers wishing to understand the fundamental principles and research problems underpinning the smart grid ecosystem, and the main mathematical tools used to model and analyze such systems

Modeling and optimization of the smart grid ecosystem

The smart energy grid has evolved into a complex ecosystem, with new entering actors such as aggregators, and traditional ones like consumers, operators and generators having fundamentally different, active roles in the system. In addition, advances in key technologies such as renewables, energy storage, communication and control have paved the way to new research directions and problems. In this work we attempt to give some structure to the complex ecosystem above, and we present key research problems that shape the area. The emphasis is on the control and optimization methodology toward approaching these problems. The first thread we consider is demand-response where the central theme is to optimize the demand load of consumers. The basic problem is the scheduling of demand load of consumers with the aim to minimize a cost function from the point of view of the utility operator or the consumer. Next, we review fundamental problems in energy storage management. The basic energy storage management problem amounts to deciding when and how much to charge and discharge the battery in order to achieve a certain optimization objective, either in terms of a generation cost or a mismatch between energy demand and supply, which again may capture the goals of the consumer or the utility. We also discuss the market interactions of various entities in the smart grid ecosystem and the impact of their strategic decisions on the market structure. Finally, we study key aspects of consumer behavior such as response to gamification models, and uncertainty due to consumer decisions that influence the system, and we discuss the role of data in building data-driven models for predicting consumer behavior. For each problem instance above, we provide an exposition that places emphasis on the related model and on key aspects of the analysis. © 2016 I. Koutsopoulos, T. P. Papaioannou, and V. Hatzi