Sindhu Suresh

Towards next generation photovoltaic inverters

Solar energy is under push to reach “grid parity” without additional subsidies and favorable policies. While cost and reliability are major concerns for both photovoltaic (PV) panels and PV inverters, comparable or exceeded grid functions and power quality can further help solar power become competitive to conventional generation technologies in the wholesale electricity market. This paper gives an overview of future development trends of PV inverters and proposes new requirements for next generation PV inverters under smart grid and/or microgrid environments. Approaches to address these requirements are also discussed from the research methodology perspectives. The goal of this paper is to draw the interests of the industry and academia in the new technical challenges of next generation smart PV inverters in addition to the “dollar per watt” overall PV system cost target.

Economic dispatch for a microgrid considering renewable energy cost functions

Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.

Survivability models for the assessment of smart grid distribution automation network designs

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index (SAIDI), accounting for the fact that after a failure the energy demand and supply will vary over time during a multi-step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In the special case where demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. We have evaluated the presented model using data from a real power distribution grid and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network.

Survivability analysis of power distribution in smart grids with active and reactive power modeling

A paradigm shift is taking place in the realm of power distribution networks. Power distribution networks that have been traditionally built to meet peak demand are now being automated to offer reliability on demand, i.e., smart distribution power grids can be automatically reconfigured after events such as power failures. In future distribution automation networks an important design decision will consist of which approach to use to avoid voltage drops. A standard approach is to add static capacitors to the distribution circuit. Novel techniques include the automatic reduction of active or reactive load through demand response, or the addition of distributed generators that can tradeoff active load for reactive load. In this paper, we introduce a new modeling approach to assist in such design decisions. The survivability of a system is its ability to function during and after a failure. In survivability analysis, the initial state of the system is set to a failure state, so survivability is “conditional performability” [9, 11]. The main contribution of this paper is the development of a model to study the power distribution in smart grids during the (transient) period that starts after a failure till the system fully recovers. The proposed model bridges power flow modeling of reactive power compensation [8, 14] with performability/survivability modeling of automation distribution networks [1]. We use a Markov chain to characterize the phased recovery of the system after a failure [5]. Then, we associate to each state of the Markov chain a set of corresponding rewards to characterize the active and reactive power supplied and demanded in that state.

A common analysis framework for smart distribution networks applied to survivability analysis of distribution automation

Smart distribution networks shall improve the efficiency and reliability of power distribution by intelligently managing the available power and requested load. Such intelligent power networks pose challenges for information and communication technology (ICT). Their design requires a holistic assessment of traditional power system topology and ICT architecture. Existing analysis approaches focus on analyzing the power networks components separately. For example, communication simulation provides failure data for communication links, while power analysis makes predictions about the stability of the traditional power grid. However, these insights are not combined to provide a basis for design decisions for future smart distribution networks. In this paper, we describe a common model-driven analysis framework for smart distribution networks based on the Common Information Model (CIM). This framework provides scalable analysis of large smart distribution networks by supporting analyses on different levels of abstraction. Furthermore, we apply our framework to holistic survivability analysis. We map the CIM on a survivability model to enable assessing design options with respect to the achieved survivability improvement. We demonstrate our approach by applying the mapping transformation in a case study based on a real distribution circuit. We conclude by evaluating the survivability impact of three investment options.

Modeling of a small distribution grid with intermittent energy resources using MATLAB/SIMULINK

Renewable Energy resources are growing exponentially, demanding for more studies in the field of integration. The penetration of these resources has grown to a level which demands structural and functional changes to the grid system to accommodate variable energy resources resulting in smart grid topology. Behavior of the grid depends upon the type of intermittent energy resources being added, point of coupling impedance and the load distribution along the system. As most of these integration activities take place on the distribution side of power network, it is desired to conduct a comprehensive analysis at the low voltage level. This paper presents the results on modeling and dynamic analysis of a distribution grid system with different levels and types of renewable energy resources using IEEE 34 bus system as a candidate testbed. The complete system is modeled and analyzed using Matlab/Simulink.

A Scalable Approach to the Assessment of Storm Impact in Distributed Automation Power Grids

We present models and metrics for the survivability assessment of distribution power grid networks accounting for the impact of multiple failures due to large storms. The analytical models used to compute the proposed metrics are built on top of three design principles: state space factorization, state aggregation, and initial state conditioning. Using these principles, we build scalable models that are amenable to analytical treatment and efficient numerical solution. Our models capture the impact of using reclosers and tie switches to enable faster service restoration after large storms.We have evaluated the presented models using data from a real power distribution grid impacted by a large storm: Hurricane Sandy. Our empirical results demonstrate that our models are able to efficiently evaluate the impact of storm hardening investment alternatives on customer affecting metrics such as the expected energy not supplied until complete system recovery.

Assessing survivability to support power grid investment decisions

Abstract The reliability of power grids has been subject of study for the past few decades. Traditionally, detailed models are used to assess how the system behaves after failures. Such models, based on power flow analysis and detailed simulations, yield accurate characterizations of the system under study. However, they fall short on scalability. In this paper, we propose an efficient and scalable approach to assess the survivability of power systems. Our approach takes into account the phased-recovery of the system after a failure occurs. The proposed phased-recovery model yields metrics such as the expected accumulated energy not supplied between failure and full recovery. Leveraging the predictive power of the model, we use it as part of an optimization framework to assist in investment decisions. Given a budget and an initial circuit to be upgraded, we propose heuristics to sample the solution space in a principled way accounting for survivability-related metrics. We have evaluated the feasibility of this approach by applying it to the design of a benchmark distribution automation circuit. Our empirical results indicate that the combination of survivability and power flow analysis can provide meaningful investment decision support for power systems engineers.

Design of distribution automation networks using survivability modeling and power flow equations

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams, smart grids require a unified and holistic approach taking into consideration the interplay of distributed generation, distribution automation topology, intelligent features, and others. In this paper, we use transient survivability metrics to create better distribution automation network designs. Our approach combines survivability analysis and power flow analysis to assess the survivability of the distribution power grid network. Additionally, we present an initial approach to automatically optimize available investment decisions with respect to survivability and investment costs. We have evaluated the feasibility of this approach by applying it to the design of a real distribution automation circuit. Our empirical results indicate that the combination of survivability analysis and power flow can provide meaningful investment decision support for power systems engineers.

Methodology for the optimal design of PEV charging systems with multiple chargers and distributed resources

Increased penetration of plug-in electric vehicles (PEVs) will necessitate deployment of numerous PEV chargers. Pairing these chargers with renewable distributed generation (DG) and storage can potentially alleviate negative impacts on the distribution grid and help meet renewable portfolio goals. The optimal design of such integrated charging systems depends on many factors, including geographic location and charging profiles. This paper presents an optimization methodology for designing integrated PEV charging systems with multiple chargers and distributed resources. This methodology is used to investigate optimal designs for charging systems at a retail business and on a university campus. When PEV charging can introduce a demand charge, it is shown that the optimal design depends on the time of charging and the level of existing load. When non-negligible distribution system losses exist between charger locations, it is shown that the optimal size and location of DG and storage depends on the charging profile of the different chargers and the distribution efficiency.