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Energy storage can generate significant revenue by taking advantage of fluctuations in real-time energy market prices. In this paper, we investigate the real-time price arbitrage potential of aerodynamic energy storage in wind farms. This under-explored source of energy storage can be realized by deferring energy extraction by turbines toward the front of a farm for later extraction by downstream turbines. In large wind farms, this kinetic energy can be stored for minutes to tens of minutes, depending on the inter-turbine travel distance and the incoming wind speed. This storage mechanism requires minimal capital costs for implementation and potentially could provide additional revenue to wind farm operators. We demonstrate that the potential for revenue generation depends on the energy arbitrage (storage) efficiency and the wind travel time between turbines. We then characterize how price volatility and arbitrage efficiency affect real-time energy market revenue potential. Simulation results show that when price volatility is low, which is the historic norm, noticeably increased revenue is only achieved with high arbitrage efficiencies. However, as price volatility increases, which is expected in the future as the composition of the power system evolves, revenues increase by several percent. 

Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented scheme within a large DSO. 

We propose a novel state estimation algorithm for consensus dynamics subject to measurement error. We first demonstrate that with properly tuned parameters, our algorithm attains the same equilibrium value that would be attained using the traditional algorithm based on local state feedback (nominal consensus). We then show that our approach improves consensus performance in a particular class of problems by reducing the state error (i.e., the difference between the agent states and the consensus value). A numerical example compares the performance of the distributed algorithm we propose to that of the traditional local feedback scheme. The results show that the proposed algorithm significantly reduces the state error. 

his work investigates the potential of using aggregate controllable loads and energy storage systems from multiple heterogeneous feeders to jointly optimize a utility's energy procurement cost from the real-time market and their revenue from ancillary service markets. Toward this, we formulate an optimization problem that co-optimizes real-time and energy reserve markets based on real-time and ancillary service market prices, along with available solar power, storage and demand data from each of the feeders within a single distribution network. The optimization, which includes all network system constraints, provides real/reactive power and energy storage set-points for each feeder as well as a schedule for the aggregate system's participation in the two types of markets. We evaluate the performance of our algorithm using several trace-driven simulations based on a real-world circuit of a New Jersey utility. The results demonstrate that active participation through controllable loads and storage significantly reduces the utility's net costs, i.e., real-time energy procurement costs minus ancillary market revenues. 

Consensus algorithms constitute a powerful tool for computing average values or coordinating agents in many distributed applications. Unfortunately, the same property that allows this computation (i.e., the nontrivial nullspace of the state matrix) leads to unbounded state variance in the presence of measurement errors. In this work, we explore the trade-off between relative and absolute communication (feedback) in the presence of measurement errors. We evaluate the robustness of first and second-order integrator systems under a parameterized family of controllers (homotopy), that continuously trade between relative and absolute feedback interconnections, in terms of the H 2 norm of an appropriately defined input-output system. Our approach extends the previous H 2 norm-based analysis to systems with directed feedback interconnections whose underlying weighted graph Laplacians are diagonalizable. Our results indicate that any level of absolute communication is sufficient to achieve a finite H 2 norm, but purely relative feedback can only achieve finite norms when the measurement error is not exciting the subspace associated with the consensus state. Numerical examples demonstrate that smoothly reducing the proportion of absolute feedback in double integrator systems smoothly decreases the system performance (increases the H 2 norm) and that this performance degradation is more rapid in systems with relative feedback in only the first state (position).