Anupam A. Thatte

Risk Measure Based Robust Bidding Strategy for Arbitrage Using a Wind Farm and Energy Storage

This paper proposes the use of a risk measure based robust optimization bidding strategy for dispatching a wind farm in combination with energy storage. Through coordination with energy storage devices, variable wind generators can be utilized as dispatchable energy producers in the deregulated electricity market. The total profit from sale of electricity can be increased by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day ahead market. A case study is presented to show that as the forecast error in electricity price increases, the robust optimization based bidding strategy has an increasing probability of yielding better economic performance than a deterministic optimization based bidding strategy. The uncertainty set for robust optimization is selected based on the coherent risk measure conditional value at risk (CVaR). Uncertainties in electricity price forecasting and wind power forecasting are considered. The resulting robust optimization based bidding strategy is evaluated using Monte Carlo simulation for different choices of uncertainty sets.

Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation

The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses significant challenges to system operators. To ensure reliable operation of power systems, accurate forecasting of PV power production is essential. In this paper, we propose a novel multitime-scale data-driven forecast model to improve the accuracy of short-term PV power production. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared to the conventional persistence (PSS) model. The tradeoff between computation cost and improved forecast quality is studied using real datasets from PV sites in California and Colorado.

Towards a Unified Operational Value Index of Energy Storage in Smart Grid Environment

This paper proposes a unified operational value index of energy storage in the smart grid electricity market environment. Given the heterogeneity of many storage service providers enabled by smart grid technologies, this unified value index will allow for comparison across different technology choices. It is also argued that implicit forms of energy storage, such as demand response, should be valued and utilized. A cross-market cooptimization is proposed to maximize the operational value of energy storage under the deregulated electricity market environment. For numerical illustration a case study is conducted in a modified 24-bus IEEE Reliability Test System (RTS), which includes flywheels, battery storage in the form of plug-in electric vehicles, and price responsive thermal load.

Coordination of wind farms and flywheels for energy balancing and frequency regulation

In this paper we propose a control scheme to coordinate wind generators and flywheel energy storage systems (FESS) for the provision of energy balancing and grid frequency regulation services. By exploiting power electronics-based controllers to adjust the output power from these asynchronous machines, the proposed control enables both wind generators and FESS to collectively respond to the system frequency deviations. Therefore, the seemingly non-dispatchable wind can be utilized to improve the system frequency performance and reduce the regulation burden on conventional generators. Economically, by allowing wind to participate in regulation markets, the proposed control extracts more value from the variable wind generation and reduces the system frequency regulation costs. Moreover, coordination of wind and FESS could increase the profits for both the wind generators and the FESS. We demonstrate the technical and economic performances of the proposed control in a small five bus system.

Robust Optimization Based Economic Dispatch for Managing System Ramp Requirement

The increasing penetration of renewable generation poses a challenge to the power system operators task of balancing demand with generation due to the increased inter-temporal variability and uncertainty from renewables. Recently major system operators have been testing approaches to managing inter-temporal ramping requirement. In this paper we propose a robust optimization based economic dispatch model for ensuring adequate system ramp capability. The proposed model is critically assessed with the ramp product which is currently under consideration by several system operators. We conduct theoretical assessment based on a proposed lack-of-ramp probability (LORP) index and numerical assessment using Monte Carlo simulations. It is shown that compared with the recently proposed industry model, the proposed robust formulation of ramp requirement yields more smoothed generation cost variation and is capable of ensuring lower lack of ramp probability.

Robust bidding strategy for wind power plants and energy storage in electricity markets

This paper explores a robust optimization-based bidding strategy for operating a wind farm in combination with energy storage devices in electricity markets. Through coordination with moderate capacity of energy storage, variable wind resources can be utilized in multi-time-scale electricity market operations, as opposed to being utilized only as real-time non-dispatchable energy producers. Given the inherent uncertainties in electricity market prices and available wind generator output, a robust optimization-based approach is formulated to determine the bidding strategy. Case studies on day-ahead and hour-ahead markets show that robust-optimization based bidding strategy provides computationally practical and economically efficient approach to operating wind farms and co-located storage when uncertainties are severe.

A Metric and Market Construct of Inter-Temporal Flexibility in Time-Coupled Economic Dispatch

The increasing penetration of variable and uncertain generation from renewable resources poses a challenge for balancing the power system supply and demand. Violations of this constraint seriously impact system reliability and thus carry with them a very high cost. In order to address the issues created by variable generation increased system flexibility is required. System operators are considering modifications to the conventional real-time economic dispatch model to provide improved ramp flexibility. In this paper an operational flexibility metric called lack of ramp probability (LORP) is proposed for the real-time economic dispatch. Further, a two-step robust optimization based framework is introduced to simultaneously guarantee LORP flexibility metric and ensure ramp deliverability in a multi-zonal setting. The proposed framework is illustrated on a 3-zone modified IEEE 73 bus (RTS-96) test system.

Frequency aware economic dispatch

In this paper we propose a unified economic dispatch and frequency control framework for power systems with an increasing amount of intermittent resources such as wind and solar. While the conventional hierarchies of power system decision and control are based on the time-scale separation principles (e.g., primary, secondary, and tertiary control), the increasing penetration of variable and uncertain resources may render the assumption of time-scale separation invalid. Therefore, the decision and control obtained from the conventional hierarchies will be suboptimal in its nature. In this paper, a model-based joint economic dispatch and frequency control scheme is proposed to improve the efficiency of generation control under the new set of assumptions. Enabled by advances in faster and cheaper sensing, communication, and computing capabilities, the proposed frequency aware economic dispatch (FAED) is readily implementable for todays power system operations. In a two-area test system simulation, we illustrate that the proposed FAED complies with the present frequency performance criteria and reduces the total generation cost.

A robust model predictive control approach to coordinating wind and storage for joint energy balancing and frequency regulation services

In this paper a novel approach is proposed to coordinate wind generators and battery energy storage systems (BESS) to provide both energy balancing and frequency regulation services in electricity markets. A robust optimization based model predictive control (RMPC) scheme is developed to determine the optimal bidding strategy for wind and storage, under uncertainty due to electricity price forecast error. Allowing the wind generation and battery energy storage combination to participate in both the energy balancing and frequency regulation markets increases their joint profits. The performance of the proposed Robust MPC-based optimal bidding strategy is demonstrated through a case study.

A theory for the economic operation of a smart grid with stochastic renewables, demand response and storage

We are motivated by the problems faced by independent system operators in an era where renewables constitute a significant portion of generation and demand response is employed by a significant portion of loads. We address a key issue of designing architectures and algorithms which generate optimal demand response over a time window in a decentralized manner, for a smarter grid consisting of several stochastic renewables and dynamic loads. By optimal demand response, we refer to the demand response which maximizes the sum of the utilities of the agents, i.e., generators, loads, load serving entities, storage services, prosumers, etc., connected to the smart-grid. By decentralized we refer to the desirable case where neither the independent system operator (ISO) needs to know the dynamics/utilities/states of the agents, nor do the agents need to know the dynamics/utilities/states of each other. The communication between the ISO and agents is restricted to the ISO announcing prices, and the agents responding with their energy generation/consumption bids. We begin with the deterministic case for which there is a complete solution. It features a price iteration scheme that results in optimality of social welfare. We also provide an optimal solution for the case where there is a common randomness affecting and observed by all agents. This solution can be computationally complex, though we provide approximations. For the more general partially observed randomness case, we exhibit a relaxation that significantly reduces complexity. We also provide an approximation strategy that leads to a model predictive control (MPC) approach. Simulation results illustrate the increase in social welfare utility compared to some alternative architectures.