Showing papers by "Rajiv K. Varma published in 2017"

SSR Mitigation With a New Control of PV Solar Farm as STATCOM (PV-STATCOM)

Abstract: This paper presents a novel control of a large-scale PV solar farm as STATCOM, termed PV-STATCOM, for alleviation of subsynchronous resonance (SSR) in a steam turbine driven synchronous generator connected to a series compensated transmission line. During nighttime, the PV solar farm can operate as a STATCOM with its entire inverter capacity for SSR mitigation. During daytime, if a system fault triggers SSR, the solar farm autonomously discontinues its normal active power generation and releases its entire inverter capacity to operate as PV-STATCOM for SSR prevention. Once the subsynchronous resonances are damped, the solar farm returns to its normal real power production. Electromagnetic transients studies using EMTDC/PSCAD are performed to demonstrate that a solar farm connected at the terminals of synchronous generator in the IEEE First SSR Benchmark system can damp all the four torsional modes at all the four critical levels of series compensation, and return to normal PV power production in less than half a minute. This proposed PV-STATCOM technology can either obviate or reduce the need of an expensive flexible ac transmission system device to accomplish the same objective. Furthermore, this technology is more than an order of magnitude cheaper than a conventional static var compensators or STATCOM of similar size.

A Real-Time Multistep Optimization-Based Model for Scheduling of Storage-Based Large-Scale Electricity Consumers in a Wholesale Market

Abstract: A new real-time optimal scheduling model is proposed and analyzed in this paper to aggregate storage benefits for a large-scale electricity consumer The complete model for optimal operation of storage-based electrical loads considering both the capital and operating expenditures of storage is developed A real-time load forecaster is incorporated into the optimal scheduling algorithm using soft constraints, slack variables, and penalizing mechanisms The application of the proposed model to a real-world large-scale electricity consumer is examined and compared with previous models It is demonstrated that the proposed model outperforms prior models by generating higher profitability of investment in storage, lower storage operating expenditure, and an extended life of the storage plant

Optimal scheduling of independently operated, locally controlled energy storage systems as dispatchable assets in a competitive electricity market

Abstract: Independently operated energy storage can exploit arbitrage opportunities available due to inter-temporal variation of electricity prices. Storage can be utilised as a dispatchable or non-dispatchable asset. In this study, a new optimal scheduling algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by independent system operators (ISOs). Storage in this case is referred to as dispatchable storage. In addition, a new index is proposed to measure the storage dispatchability. While the operation of locally controlled storage is optimally scheduled at the owner's end, using the proposed algorithm, storage is fully dispatchable at the ISO's end. Dispatchable storage units have great potential to enhance the flexibility of electric grids and are key elements envisioned to enable smart grid realisation. The proposed algorithm outperforms previous algorithms in which storage is either locally controlled at the owner's end and cannot optimally accept ISO's instructions; or storage is centrally controlled by the grid operator to achieve some technical objectives. The efficacy and feasibility of the proposed algorithm are validated using real-world data. It is demonstrated that the proposed algorithm can enable the storage to accept all ISO's instructions. Revenue values of dispatchable and non-dispatchable storage are computed and compared.

Investigation of Ontario's electricity market behaviour and energy storage scheduling in the market based on model predictive control

Abstract: In this paper, comprehensive studies are conducted over Ontario's electricity market data in the past decade. It is indicated that the increase in renewable energy penetration into the market has altered the overall behaviour of the energy market, with negative electricity prices even starting to appear. As a result, deployment of large-scale storage units in the market is considered to address the negative price issues. A compressed-air storage unit is selected and modeled as a large-scale storage option for numerical studies. An optimization-based algorithm based on model predictive control is proposed for optimal scheduling of storage. Real-world data adopted from Ontario's market are used for numerical studies. The storage arbitrage profit in the market is computed and analyzed. It is concluded that storage arbitrage profit significantly increases using model predictive control as compared to a self-scheduling approach, thereby storage becomes more profitable.