IoT-Enabled Real-Time Management of Smart Grids with Demand Response Aggregators

Integration of widely distributed small-scale Renewable Energy Sources like rooftop Photovoltaic panels and emerging loads like plug-in Electric Vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this paper, we present a developed real-time management schema based on Internet of Things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified Optimal Power Flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulation- based test bed and realised the performance of this approach in a real-like environment using real data of a network with residential buildings

Coordination of impedance controllers and flexible power for curative congestion management in real-time applications

As the share of feed-in from renewable energy sources rises in German electricity grids, established preventive congestion management processes are called into question. Curative congestion management may increase the utilization of already existing grid capacity without the large investments necessary for conventional grid expansion. The curative paradigm shift requires fast reacting remedial measures, such as innovative power flow controlling devices, as well as reliable algorithms to determine and activate them in due time. This work shows how an automated system can coordinate distributed FACTS devices, that influence a power line's series reactance, and active power from flexible units to solve line overloads in high voltage grids. First, linear sensitivities for gradual reactance changes are derived. Based on this, an optimization and a heuristic approach for automated curative coordination of both types of remedial measures is conceptualized as well as implemented and tested in simulations. Then, the heuristic approach is implemented within a distributed agent-based control algorithm, along with fallback strategies to be executed if agent communication fails. This system is then tested in a laboratory setup to evaluate its real-time applicability. The laboratory setup consists of multiple (Power) Hardware-in-the-Loop modules to create an experimental environment considering many real-world factors that are usually neglected in software simulations. This way, not just the agent algorithm itself, but also the influence of communication delays, reaction times of real power flow controlling devices as well as the integration into a control center environment are evaluated

Comparison of the performance of sensitivity-based voltage control algorithms in DG-integrated distribution systems

Conference ProceedingsThe integration of renewable energy generators in distribution grids has increased the complexity of the voltage control problem. Reactive power control (RPC) algorithms based on sensitivity analysis have been proposed in the literature for the management of the voltage problem. Sensitivity methods are computationally complex for practical real-time analysis and this has led to use of de-coupled and other simplified load flow models. However, algorithms based on decoupled models have been shown to be inefficient for analysis of distribution systems with low X/R ratio. This paper uses a simplified line modelling approach recently proposed in the literature to facilitate the development of computationally simple distributed, non-decoupled, load flow equations that completely capture the characteristics of the radial distribution feeder, removing the need to use the decoupled models. Results show that the simple algorithm based on this new line modelling approach gives better voltage control performance compared to the decoupled models

Challenges to the Integration of Renewable Resources at High System Penetration

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches

Distribution System Voltage Management and Optimization for Integration of Renewables and Electric Vehicles: Research Gap Analysis

California is striving to achieve 33% renewable penetration by 2020 in accordance with the state’s Renewable Portfolio Standard (RPS). The behavior of renewable resources and electric vehicles in distribution systems is creating constraints on the penetration of these resources into the distribution system. One such constraint is the ability of present-­‐‑day voltage management methodologies to maintain proper distribution system voltage profiles in the face of higher penetrations of PV and electric vehicle technologies. This white paper describes the research gaps that have been identified in current Volt/VAR Optimization and Control (VVOC) technologies, the emerging technologies which are becoming available for use in VVOC, and the research gaps which exist and must be overcome in order to realize the full promise of these emerging technologies

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Limits on the Benefits of Energy Storage for Renewable Integration

The high variability of renewable energy resources presents significant challenges to the operation of the electric power grid. Conventional generators can be used to mitigate this variability but are costly to operate and produce carbon emissions. Energy storage provides a more environmentally friendly alternative, but is costly to deploy in large amounts. This paper studies the limits on the benefits of energy storage to renewable energy: How effective is storage at mitigating the adverse effects of renewable energy variability? How much storage is needed? What are the optimal control policies for operating storage? To provide answers to these questions, we first formulate the power flow in a single-bus power system with storage as an infinite horizon stochastic program. We find the optimal policies for arbitrary net renewable generation process when the cost function is the average conventional generation (environmental cost) and when it is the average loss of load probability (reliability cost). We obtain more refined results by considering the multi-timescale operation of the power system. We view the power flow in each timescale as the superposition of a predicted (deterministic) component and an prediction error (residual) component and formulate the residual power flow problem as an infinite horizon dynamic program. Assuming that the net generation prediction error is an IID process, we quantify the asymptotic benefits of storage. With the additional assumption of Laplace distributed prediction error, we obtain closed form expressions for the stationary distribution of storage and conventional generation. Finally, we propose a two-threshold policy that trades off conventional generation saving with loss of load probability. We illustrate our results and corroborate the IID and Laplace assumptions numerically using datasets from CAISO and NREL.Comment: 45 pages, 17 figure

Ancillary Services in Hybrid AC/DC Low Voltage Distribution Networks

In the last decade, distribution systems are experiencing a drastic transformation with the advent of new technologies. In fact, distribution networks are no longer passive systems, considering the current integration rates of new agents such as distributed generation, electrical vehicles and energy storage, which are greatly influencing the way these systems are operated. In addition, the intrinsic DC nature of these components, interfaced to the AC system through power electronics converters, is unlocking the possibility for new distribution topologies based on AC/DC networks. This paper analyzes the evolution of AC distribution systems, the advantages of AC/DC hybrid arrangements and the active role that the new distributed agents may play in the upcoming decarbonized paradigm by providing different ancillary services.Ministerio de Economía y Competitividad ENE2017-84813-RUnión Europea (Programa Horizonte 2020) 76409